{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract Icesat2 data (This code is validate only after downloading HDF5 file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "# Our data folder \n",
    "data_home = Path('/home/jovyan/shared/data-Jun/Data')\n",
    "\n",
    "# Create folder if it doesn't exist\n",
    "data_home.mkdir(exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from icepyx import icesat2data as ipd\n",
    "\n",
    "short_name = 'ATL06'\n",
    "spatial_extent = [-138.3990209216774,58.85936873949439,-137.44559547328527, 59.306828871347534]  # MLO MODIFIED\n",
    "date_range = ['2018-10-14','2020-06-16']\n",
    "\n",
    "region = ipd.Icesat2Data(short_name, spatial_extent, date_range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "product:     ATL06\n",
      "dates:       ['2018-10-14', '2020-06-16']\n",
      "start time:  00:00:00\n",
      "end time:    23:59:59\n",
      "version:     003\n",
      "extent:      ['bounding box', [-138.3990209216774, 58.85936873949439, -137.44559547328527, 59.306828871347534]]\n",
      "\n",
      "DATA:\n",
      "('Number of available granules', 70)\n",
      "('Average size of granules (MB)', 4.264068085807144)\n",
      "('Total size of all granules (MB)', 298.48476600649997)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fc8c9705d3b44f30b9038dd52f0e78d2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib widget\n",
    "print('product:    ', region.dataset)\n",
    "print('dates:      ', region.dates)\n",
    "print('start time: ', region.start_time)\n",
    "print('end time:   ', region.end_time)\n",
    "print('version:    ', region.dataset_version)\n",
    "print('extent:     ', region.spatial_extent)\n",
    "\n",
    "print('\\nDATA:')\n",
    "print('\\n'.join([str(item) for item in region.avail_granules().items()]))\n",
    "\n",
    "region.visualize_spatial_extent()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can dowload the data by using the below code. I don't recommend it. It would be better to skip next code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Earthdata Login password:  ···············\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Invalid username or password, please retry.']\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Please re-enter your Earthdata user ID:  michael_loso@nps.gov\n",
      "Earthdata Login password:  ···············\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Invalid username or password, please retry.']\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Please re-enter your Earthdata user ID:  michael_loso@nps.gov\n",
      "Earthdata Login password:  ···············\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Invalid username or password, please retry.']\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Please re-enter your Earthdata user ID:  michael_loso@nps.gov\n",
      "Earthdata Login password:  ···············\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Invalid username or password, please retry.']\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Please re-enter your Earthdata user ID:  mycloso\n",
      "Earthdata Login password:  ···············\n"
     ]
    }
   ],
   "source": [
    "name = 'mycloso'   # MLO MODIFIED\n",
    "email = 'michael_loso@nps.gov'   # MLO MODIFIED\n",
    "\n",
    "# Only download if data folder is empty\n",
    "if not list(data_home.glob('*.h5')):\n",
    "    region.earthdata_login(name, email)\n",
    "    region.download_granules(data_home)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run it from here again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/jovyan/shared/data-Jun/Data/ATL06_20191029184431_05020505_003_01.h5\n",
      "/home/jovyan/shared/data-Jun/Data/ATL06_20191204043607_10430502_003_01.h5\n",
      "/home/jovyan/shared/data-Jun/Data/ATL06_20191021190111_03800505_003_01.h5\n",
      "/home/jovyan/shared/data-Jun/Data/ATL06_20191122050939_08600503_003_01.h5\n",
      "/home/jovyan/shared/data-Jun/Data/ATL06_20191126050119_09210503_003_01.h5\n",
      "/home/jovyan/shared/data-Jun/Data/ATL06_20191225033720_13630503_003_01.h5\n",
      "/home/jovyan/shared/data-Jun/Data/ATL06_20191126045248_09210502_003_01.h5\n",
      "/home/jovyan/shared/data-Jun/Data/ATL06_20191221034540_13020503_003_01.h5\n",
      "/home/jovyan/shared/data-Jun/Data/ATL06_20191118050929_07990502_003_01.h5\n",
      "/home/jovyan/shared/data-Jun/Data/ATL06_20191130044427_09820502_003_01.h5\n",
      "Total number of files: 24\n"
     ]
    }
   ],
   "source": [
    "files = list(data_home.glob('*.h5'))\n",
    "\n",
    "for f in files[:10]: print(f)\n",
    "print('Total number of files:', len(files))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/                        Group\n",
      "/METADATA                Group\n",
      "/METADATA/AcquisitionInformation Group\n",
      "/METADATA/AcquisitionInformation/lidar Group\n",
      "/METADATA/AcquisitionInformation/lidarDocument Group\n",
      "/METADATA/AcquisitionInformation/platform Group\n",
      "/METADATA/AcquisitionInformation/platformDocument Group\n",
      "/METADATA/DataQuality    Group\n",
      "/METADATA/DataQuality/CompletenessOmission Group\n",
      "/METADATA/DataQuality/DomainConsistency Group\n",
      "/METADATA/DatasetIdentification Group\n",
      "/METADATA/Extent         Group\n",
      "/METADATA/Lineage        Group\n",
      "/METADATA/Lineage/ANC06-01 Group\n",
      "/METADATA/Lineage/ANC06-02 Group\n",
      "/METADATA/Lineage/ANC06-03 Group\n",
      "/METADATA/Lineage/ANC17  Group\n",
      "/METADATA/Lineage/ANC19  Group\n",
      "/METADATA/Lineage/ANC25-06 Group\n",
      "/METADATA/Lineage/ANC26-06 Group\n",
      "/METADATA/Lineage/ANC28  Group\n",
      "/METADATA/Lineage/ANC36-06 Group\n",
      "/METADATA/Lineage/ANC38-06 Group\n",
      "/METADATA/Lineage/ATL03  Group\n",
      "/METADATA/Lineage/ATL09  Group\n",
      "/METADATA/Lineage/Control Group\n",
      "/METADATA/ProcessStep    Group\n",
      "/METADATA/ProcessStep/Browse Group\n",
      "/METADATA/ProcessStep/Metadata Group\n",
      "/METADATA/ProcessStep/PGE Group\n",
      "/METADATA/ProcessStep/QA Group\n",
      "/METADATA/ProductSpecificationDocument Group\n",
      "/METADATA/QADatasetIdentification Group\n",
      "/METADATA/SeriesIdentification Group\n",
      "/ancillary_data          Group\n",
      "/ancillary_data/atlas_sdp_gps_epoch Dataset {1}\n",
      "/ancillary_data/control  Dataset {1}\n",
      "/ancillary_data/data_end_utc Dataset {1}\n",
      "/ancillary_data/data_start_utc Dataset {1}\n",
      "/ancillary_data/end_cycle Dataset {1}\n",
      "/ancillary_data/end_delta_time Dataset {1}\n",
      "/ancillary_data/end_geoseg Dataset {1}\n",
      "/ancillary_data/end_gpssow Dataset {1}\n",
      "/ancillary_data/end_gpsweek Dataset {1}\n",
      "/ancillary_data/end_orbit Dataset {1}\n",
      "/ancillary_data/end_region Dataset {1}\n",
      "/ancillary_data/end_rgt  Dataset {1}\n",
      "/ancillary_data/granule_end_utc Dataset {1}\n",
      "/ancillary_data/granule_start_utc Dataset {1}\n",
      "/ancillary_data/land_ice Group\n",
      "/ancillary_data/land_ice/dt_hist Dataset {1}\n",
      "/ancillary_data/land_ice/fit_maxiter Dataset {1}\n",
      "/ancillary_data/land_ice/fpb_maxiter Dataset {1}\n",
      "/ancillary_data/land_ice/max_res_ids Dataset {1}\n",
      "/ancillary_data/land_ice/maxiter Dataset {1}\n",
      "/ancillary_data/land_ice/min_dist Dataset {1}\n",
      "/ancillary_data/land_ice/min_gain_th Dataset {1}\n",
      "/ancillary_data/land_ice/min_n_pe Dataset {1}\n",
      "/ancillary_data/land_ice/min_n_sel Dataset {1}\n",
      "/ancillary_data/land_ice/min_signal_conf Dataset {1}\n",
      "/ancillary_data/land_ice/n_hist Dataset {1}\n",
      "/ancillary_data/land_ice/n_sigmas Dataset {1}\n",
      "/ancillary_data/land_ice/nhist_bins Dataset {1}\n",
      "/ancillary_data/land_ice/proc_interval Dataset {1}\n",
      "/ancillary_data/land_ice/qs_lim_bsc Dataset {1}\n",
      "/ancillary_data/land_ice/qs_lim_hrs Dataset {1}\n",
      "/ancillary_data/land_ice/qs_lim_hsigma Dataset {1}\n",
      "/ancillary_data/land_ice/qs_lim_msw Dataset {1}\n",
      "/ancillary_data/land_ice/qs_lim_snr Dataset {1}\n",
      "/ancillary_data/land_ice/qs_lim_sss Dataset {1}\n",
      "/ancillary_data/land_ice/rbin_width Dataset {1}\n",
      "/ancillary_data/land_ice/sigma_beam Dataset {1}\n",
      "/ancillary_data/land_ice/sigma_tx Dataset {1}\n",
      "/ancillary_data/land_ice/t_dead Dataset {1}\n",
      "/ancillary_data/qa_at_interval Dataset {1}\n",
      "/ancillary_data/release  Dataset {1}\n",
      "/ancillary_data/start_cycle Dataset {1}\n",
      "/ancillary_data/start_delta_time Dataset {1}\n",
      "/ancillary_data/start_geoseg Dataset {1}\n",
      "/ancillary_data/start_gpssow Dataset {1}\n",
      "/ancillary_data/start_gpsweek Dataset {1}\n",
      "/ancillary_data/start_orbit Dataset {1}\n",
      "/ancillary_data/start_region Dataset {1}\n",
      "/ancillary_data/start_rgt Dataset {1}\n",
      "/ancillary_data/version  Dataset {1}\n",
      "/gt1l                    Group\n",
      "/gt1l/land_ice_segments  Group\n",
      "/gt1l/land_ice_segments/atl06_quality_summary Dataset {29791/Inf}\n",
      "/gt1l/land_ice_segments/bias_correction Group\n",
      "/gt1l/land_ice_segments/bias_correction/fpb_mean_corr Dataset {29791/Inf}\n",
      "/gt1l/land_ice_segments/bias_correction/fpb_mean_corr_sigma Dataset {29791/Inf}\n",
      "/gt1l/land_ice_segments/bias_correction/fpb_med_corr Dataset {29791/Inf}\n",
      "/gt1l/land_ice_segments/bias_correction/fpb_med_corr_sigma Dataset {29791/Inf}\n",
      "/gt1l/land_ice_segments/bias_correction/fpb_n_corr Dataset {29791/Inf}\n",
      "/gt1l/land_ice_segments/bias_correction/med_r_fit Dataset {29791/Inf}\n",
      "/gt1l/land_ice_segments/bias_correction/tx_mean_corr Dataset {29791/Inf}\n",
      "/gt1l/land_ice_segments/bias_correction/tx_med_corr Dataset {29791/Inf}\n",
      "/gt1l/land_ice_segments/delta_time Dataset {29791/Inf}\n",
      "/gt1l/land_ice_segments/dem Group\n",
      "/gt1l/land_ice_segments/dem/dem_flag Dataset {29791/Inf}\n",
      "/gt1l/land_ice_segments/dem/dem_h Dataset {29791/Inf}\n",
      "/gt1l/land_ice_segments/dem/geoid_h Dataset {29791/Inf}\n",
      "/gt1l/land_ice_segments/fit_statistics Group\n",
      "/gt1l/land_ice_segments/fit_statistics/dh_fit_dx Dataset {29791/Inf}\n",
      "/gt1l/land_ice_segments/fit_statistics/dh_fit_dx_sigma Dataset {29791/Inf}\n",
      "/gt1l/land_ice_segments/fit_statistics/dh_fit_dy Dataset {29791/Inf}\n",
      "/gt1l/land_ice_segments/fit_statistics/h_expected_rms Dataset {29791/Inf}\n",
      "/gt1l/land_ice_segments/fit_statistics/h_mean Dataset {29791/Inf}\n",
      "/gt1l/land_ice_segments/fit_statistics/h_rms_misfit Dataset {29791/Inf}\n",
      "/gt1l/land_ice_segments/fit_statistics/h_robust_sprd Dataset {29791/Inf}\n",
      "/gt1l/land_ice_segments/fit_statistics/n_fit_photons Dataset {29791/Inf}\n",
      "/gt1l/land_ice_segments/fit_statistics/n_seg_pulses Dataset {29791/Inf}\n",
      "/gt1l/land_ice_segments/fit_statistics/sigma_h_mean Dataset {29791/Inf}\n",
      "/gt1l/land_ice_segments/fit_statistics/signal_selection_source Dataset {29791/Inf}\n",
      "/gt1l/land_ice_segments/fit_statistics/signal_selection_source_status Dataset {29791/Inf}\n",
      "/gt1l/land_ice_segments/fit_statistics/snr Dataset {29791/Inf}\n",
      "/gt1l/land_ice_segments/fit_statistics/snr_significance Dataset {29791/Inf}\n",
      "/gt1l/land_ice_segments/fit_statistics/w_surface_window_final Dataset {29791/Inf}\n",
      "/gt1l/land_ice_segments/geophysical Group\n",
      "/gt1l/land_ice_segments/geophysical/bckgrd Dataset {29791/Inf}\n",
      "/gt1l/land_ice_segments/geophysical/bsnow_conf Dataset {29791/Inf}\n",
      "/gt1l/land_ice_segments/geophysical/bsnow_h Dataset {29791/Inf}\n",
      "/gt1l/land_ice_segments/geophysical/bsnow_od Dataset {29791/Inf}\n",
      "/gt1l/land_ice_segments/geophysical/cloud_flg_asr Dataset {29791/Inf}\n",
      "/gt1l/land_ice_segments/geophysical/cloud_flg_atm Dataset {29791/Inf}\n",
      "/gt1l/land_ice_segments/geophysical/dac Dataset {29791/Inf}\n",
      "/gt1l/land_ice_segments/geophysical/e_bckgrd Dataset {29791/Inf}\n",
      "/gt1l/land_ice_segments/geophysical/layer_flag Dataset {29791/Inf}\n",
      "/gt1l/land_ice_segments/geophysical/msw_flag Dataset {29791/Inf}\n",
      "/gt1l/land_ice_segments/geophysical/neutat_delay_total Dataset {29791/Inf}\n",
      "/gt1l/land_ice_segments/geophysical/r_eff Dataset {29791/Inf}\n",
      "/gt1l/land_ice_segments/geophysical/solar_azimuth Dataset {29791/Inf}\n",
      "/gt1l/land_ice_segments/geophysical/solar_elevation Dataset {29791/Inf}\n",
      "/gt1l/land_ice_segments/geophysical/tide_earth Dataset {29791/Inf}\n",
      "/gt1l/land_ice_segments/geophysical/tide_equilibrium Dataset {29791/Inf}\n",
      "/gt1l/land_ice_segments/geophysical/tide_load Dataset {29791/Inf}\n",
      "/gt1l/land_ice_segments/geophysical/tide_ocean Dataset {29791/Inf}\n",
      "/gt1l/land_ice_segments/geophysical/tide_pole Dataset {29791/Inf}\n",
      "/gt1l/land_ice_segments/ground_track Group\n",
      "/gt1l/land_ice_segments/ground_track/ref_azimuth Dataset {29791/Inf}\n",
      "/gt1l/land_ice_segments/ground_track/ref_coelv Dataset {29791/Inf}\n",
      "/gt1l/land_ice_segments/ground_track/seg_azimuth Dataset {29791/Inf}\n",
      "/gt1l/land_ice_segments/ground_track/sigma_geo_at Dataset {29791/Inf}\n",
      "/gt1l/land_ice_segments/ground_track/sigma_geo_r Dataset {29791/Inf}\n",
      "/gt1l/land_ice_segments/ground_track/sigma_geo_xt Dataset {29791/Inf}\n",
      "/gt1l/land_ice_segments/ground_track/x_atc Dataset {29791/Inf}\n",
      "/gt1l/land_ice_segments/ground_track/y_atc Dataset {29791/Inf}\n",
      "/gt1l/land_ice_segments/h_li Dataset {29791/Inf}\n",
      "/gt1l/land_ice_segments/h_li_sigma Dataset {29791/Inf}\n",
      "/gt1l/land_ice_segments/latitude Dataset {29791/Inf}\n",
      "/gt1l/land_ice_segments/longitude Dataset {29791/Inf}\n",
      "/gt1l/land_ice_segments/segment_id Dataset {29791/Inf}\n",
      "/gt1l/land_ice_segments/sigma_geo_h Dataset {29791/Inf}\n",
      "/gt1l/residual_histogram Group\n",
      "/gt1l/residual_histogram/bckgrd_per_m Dataset {2758/Inf}\n",
      "/gt1l/residual_histogram/bin_top_h Dataset {748}\n",
      "/gt1l/residual_histogram/count Dataset {2758/Inf, 748}\n",
      "/gt1l/residual_histogram/delta_time Dataset {2758/Inf}\n",
      "/gt1l/residual_histogram/ds_segment_id Dataset {10}\n",
      "/gt1l/residual_histogram/lat_mean Dataset {2758/Inf}\n",
      "/gt1l/residual_histogram/lon_mean Dataset {2758/Inf}\n",
      "/gt1l/residual_histogram/pulse_count Dataset {2758/Inf}\n",
      "/gt1l/residual_histogram/segment_id_list Dataset {2758/Inf, 10}\n",
      "/gt1l/residual_histogram/x_atc_mean Dataset {2758/Inf}\n",
      "/gt1l/segment_quality    Group\n",
      "/gt1l/segment_quality/delta_time Dataset {29791/Inf}\n",
      "/gt1l/segment_quality/record_number Dataset {29791/Inf}\n",
      "/gt1l/segment_quality/reference_pt_lat Dataset {29791/Inf}\n",
      "/gt1l/segment_quality/reference_pt_lon Dataset {29791/Inf}\n",
      "/gt1l/segment_quality/segment_id Dataset {29791/Inf}\n",
      "/gt1l/segment_quality/signal_selection_source Dataset {29791/Inf}\n",
      "/gt1l/segment_quality/signal_selection_status Group\n",
      "/gt1l/segment_quality/signal_selection_status/signal_selection_status_all Dataset {29791/Inf}\n",
      "/gt1l/segment_quality/signal_selection_status/signal_selection_status_backup Dataset {29791/Inf}\n",
      "/gt1l/segment_quality/signal_selection_status/signal_selection_status_confident Dataset {29791/Inf}\n",
      "/gt1r                    Group\n",
      "/gt1r/land_ice_segments  Group\n",
      "/gt1r/land_ice_segments/atl06_quality_summary Dataset {29791/Inf}\n",
      "/gt1r/land_ice_segments/bias_correction Group\n",
      "/gt1r/land_ice_segments/bias_correction/fpb_mean_corr Dataset {29791/Inf}\n",
      "/gt1r/land_ice_segments/bias_correction/fpb_mean_corr_sigma Dataset {29791/Inf}\n",
      "/gt1r/land_ice_segments/bias_correction/fpb_med_corr Dataset {29791/Inf}\n",
      "/gt1r/land_ice_segments/bias_correction/fpb_med_corr_sigma Dataset {29791/Inf}\n",
      "/gt1r/land_ice_segments/bias_correction/fpb_n_corr Dataset {29791/Inf}\n",
      "/gt1r/land_ice_segments/bias_correction/med_r_fit Dataset {29791/Inf}\n",
      "/gt1r/land_ice_segments/bias_correction/tx_mean_corr Dataset {29791/Inf}\n",
      "/gt1r/land_ice_segments/bias_correction/tx_med_corr Dataset {29791/Inf}\n",
      "/gt1r/land_ice_segments/delta_time Dataset {29791/Inf}\n",
      "/gt1r/land_ice_segments/dem Group\n",
      "/gt1r/land_ice_segments/dem/dem_flag Dataset {29791/Inf}\n",
      "/gt1r/land_ice_segments/dem/dem_h Dataset {29791/Inf}\n",
      "/gt1r/land_ice_segments/dem/geoid_h Dataset {29791/Inf}\n",
      "/gt1r/land_ice_segments/fit_statistics Group\n",
      "/gt1r/land_ice_segments/fit_statistics/dh_fit_dx Dataset {29791/Inf}\n",
      "/gt1r/land_ice_segments/fit_statistics/dh_fit_dx_sigma Dataset {29791/Inf}\n",
      "/gt1r/land_ice_segments/fit_statistics/dh_fit_dy Dataset {29791/Inf}\n",
      "/gt1r/land_ice_segments/fit_statistics/h_expected_rms Dataset {29791/Inf}\n",
      "/gt1r/land_ice_segments/fit_statistics/h_mean Dataset {29791/Inf}\n",
      "/gt1r/land_ice_segments/fit_statistics/h_rms_misfit Dataset {29791/Inf}\n",
      "/gt1r/land_ice_segments/fit_statistics/h_robust_sprd Dataset {29791/Inf}\n",
      "/gt1r/land_ice_segments/fit_statistics/n_fit_photons Dataset {29791/Inf}\n",
      "/gt1r/land_ice_segments/fit_statistics/n_seg_pulses Dataset {29791/Inf}\n",
      "/gt1r/land_ice_segments/fit_statistics/sigma_h_mean Dataset {29791/Inf}\n",
      "/gt1r/land_ice_segments/fit_statistics/signal_selection_source Dataset {29791/Inf}\n",
      "/gt1r/land_ice_segments/fit_statistics/signal_selection_source_status Dataset {29791/Inf}\n",
      "/gt1r/land_ice_segments/fit_statistics/snr Dataset {29791/Inf}\n",
      "/gt1r/land_ice_segments/fit_statistics/snr_significance Dataset {29791/Inf}\n",
      "/gt1r/land_ice_segments/fit_statistics/w_surface_window_final Dataset {29791/Inf}\n",
      "/gt1r/land_ice_segments/geophysical Group\n",
      "/gt1r/land_ice_segments/geophysical/bckgrd Dataset {29791/Inf}\n",
      "/gt1r/land_ice_segments/geophysical/bsnow_conf Dataset {29791/Inf}\n",
      "/gt1r/land_ice_segments/geophysical/bsnow_h Dataset {29791/Inf}\n",
      "/gt1r/land_ice_segments/geophysical/bsnow_od Dataset {29791/Inf}\n",
      "/gt1r/land_ice_segments/geophysical/cloud_flg_asr Dataset {29791/Inf}\n",
      "/gt1r/land_ice_segments/geophysical/cloud_flg_atm Dataset {29791/Inf}\n",
      "/gt1r/land_ice_segments/geophysical/dac Dataset {29791/Inf}\n",
      "/gt1r/land_ice_segments/geophysical/e_bckgrd Dataset {29791/Inf}\n",
      "/gt1r/land_ice_segments/geophysical/layer_flag Dataset {29791/Inf}\n",
      "/gt1r/land_ice_segments/geophysical/msw_flag Dataset {29791/Inf}\n",
      "/gt1r/land_ice_segments/geophysical/neutat_delay_total Dataset {29791/Inf}\n",
      "/gt1r/land_ice_segments/geophysical/r_eff Dataset {29791/Inf}\n",
      "/gt1r/land_ice_segments/geophysical/solar_azimuth Dataset {29791/Inf}\n",
      "/gt1r/land_ice_segments/geophysical/solar_elevation Dataset {29791/Inf}\n",
      "/gt1r/land_ice_segments/geophysical/tide_earth Dataset {29791/Inf}\n",
      "/gt1r/land_ice_segments/geophysical/tide_equilibrium Dataset {29791/Inf}\n",
      "/gt1r/land_ice_segments/geophysical/tide_load Dataset {29791/Inf}\n",
      "/gt1r/land_ice_segments/geophysical/tide_ocean Dataset {29791/Inf}\n",
      "/gt1r/land_ice_segments/geophysical/tide_pole Dataset {29791/Inf}\n",
      "/gt1r/land_ice_segments/ground_track Group\n",
      "/gt1r/land_ice_segments/ground_track/ref_azimuth Dataset {29791/Inf}\n",
      "/gt1r/land_ice_segments/ground_track/ref_coelv Dataset {29791/Inf}\n",
      "/gt1r/land_ice_segments/ground_track/seg_azimuth Dataset {29791/Inf}\n",
      "/gt1r/land_ice_segments/ground_track/sigma_geo_at Dataset {29791/Inf}\n",
      "/gt1r/land_ice_segments/ground_track/sigma_geo_r Dataset {29791/Inf}\n",
      "/gt1r/land_ice_segments/ground_track/sigma_geo_xt Dataset {29791/Inf}\n",
      "/gt1r/land_ice_segments/ground_track/x_atc Dataset {29791/Inf}\n",
      "/gt1r/land_ice_segments/ground_track/y_atc Dataset {29791/Inf}\n",
      "/gt1r/land_ice_segments/h_li Dataset {29791/Inf}\n",
      "/gt1r/land_ice_segments/h_li_sigma Dataset {29791/Inf}\n",
      "/gt1r/land_ice_segments/latitude Dataset {29791/Inf}\n",
      "/gt1r/land_ice_segments/longitude Dataset {29791/Inf}\n",
      "/gt1r/land_ice_segments/segment_id Dataset {29791/Inf}\n",
      "/gt1r/land_ice_segments/sigma_geo_h Dataset {29791/Inf}\n",
      "/gt1r/residual_histogram Group\n",
      "/gt1r/residual_histogram/bckgrd_per_m Dataset {2758/Inf}\n",
      "/gt1r/residual_histogram/bin_top_h Dataset {748}\n",
      "/gt1r/residual_histogram/count Dataset {2758/Inf, 748}\n",
      "/gt1r/residual_histogram/delta_time Dataset {2758/Inf}\n",
      "/gt1r/residual_histogram/ds_segment_id Dataset {10}\n",
      "/gt1r/residual_histogram/lat_mean Dataset {2758/Inf}\n",
      "/gt1r/residual_histogram/lon_mean Dataset {2758/Inf}\n",
      "/gt1r/residual_histogram/pulse_count Dataset {2758/Inf}\n",
      "/gt1r/residual_histogram/segment_id_list Dataset {2758/Inf, 10}\n",
      "/gt1r/residual_histogram/x_atc_mean Dataset {2758/Inf}\n",
      "/gt1r/segment_quality    Group\n",
      "/gt1r/segment_quality/delta_time Dataset {29791/Inf}\n",
      "/gt1r/segment_quality/record_number Dataset {29791/Inf}\n",
      "/gt1r/segment_quality/reference_pt_lat Dataset {29791/Inf}\n",
      "/gt1r/segment_quality/reference_pt_lon Dataset {29791/Inf}\n",
      "/gt1r/segment_quality/segment_id Dataset {29791/Inf}\n",
      "/gt1r/segment_quality/signal_selection_source Dataset {29791/Inf}\n",
      "/gt1r/segment_quality/signal_selection_status Group\n",
      "/gt1r/segment_quality/signal_selection_status/signal_selection_status_all Dataset {29791/Inf}\n",
      "/gt1r/segment_quality/signal_selection_status/signal_selection_status_backup Dataset {29791/Inf}\n",
      "/gt1r/segment_quality/signal_selection_status/signal_selection_status_confident Dataset {29791/Inf}\n",
      "/gt2l                    Group\n",
      "/gt2l/land_ice_segments  Group\n",
      "/gt2l/land_ice_segments/atl06_quality_summary Dataset {29270/Inf}\n",
      "/gt2l/land_ice_segments/bias_correction Group\n",
      "/gt2l/land_ice_segments/bias_correction/fpb_mean_corr Dataset {29270/Inf}\n",
      "/gt2l/land_ice_segments/bias_correction/fpb_mean_corr_sigma Dataset {29270/Inf}\n",
      "/gt2l/land_ice_segments/bias_correction/fpb_med_corr Dataset {29270/Inf}\n",
      "/gt2l/land_ice_segments/bias_correction/fpb_med_corr_sigma Dataset {29270/Inf}\n",
      "/gt2l/land_ice_segments/bias_correction/fpb_n_corr Dataset {29270/Inf}\n",
      "/gt2l/land_ice_segments/bias_correction/med_r_fit Dataset {29270/Inf}\n",
      "/gt2l/land_ice_segments/bias_correction/tx_mean_corr Dataset {29270/Inf}\n",
      "/gt2l/land_ice_segments/bias_correction/tx_med_corr Dataset {29270/Inf}\n",
      "/gt2l/land_ice_segments/delta_time Dataset {29270/Inf}\n",
      "/gt2l/land_ice_segments/dem Group\n",
      "/gt2l/land_ice_segments/dem/dem_flag Dataset {29270/Inf}\n",
      "/gt2l/land_ice_segments/dem/dem_h Dataset {29270/Inf}\n",
      "/gt2l/land_ice_segments/dem/geoid_h Dataset {29270/Inf}\n",
      "/gt2l/land_ice_segments/fit_statistics Group\n",
      "/gt2l/land_ice_segments/fit_statistics/dh_fit_dx Dataset {29270/Inf}\n",
      "/gt2l/land_ice_segments/fit_statistics/dh_fit_dx_sigma Dataset {29270/Inf}\n",
      "/gt2l/land_ice_segments/fit_statistics/dh_fit_dy Dataset {29270/Inf}\n",
      "/gt2l/land_ice_segments/fit_statistics/h_expected_rms Dataset {29270/Inf}\n",
      "/gt2l/land_ice_segments/fit_statistics/h_mean Dataset {29270/Inf}\n",
      "/gt2l/land_ice_segments/fit_statistics/h_rms_misfit Dataset {29270/Inf}\n",
      "/gt2l/land_ice_segments/fit_statistics/h_robust_sprd Dataset {29270/Inf}\n",
      "/gt2l/land_ice_segments/fit_statistics/n_fit_photons Dataset {29270/Inf}\n",
      "/gt2l/land_ice_segments/fit_statistics/n_seg_pulses Dataset {29270/Inf}\n",
      "/gt2l/land_ice_segments/fit_statistics/sigma_h_mean Dataset {29270/Inf}\n",
      "/gt2l/land_ice_segments/fit_statistics/signal_selection_source Dataset {29270/Inf}\n",
      "/gt2l/land_ice_segments/fit_statistics/signal_selection_source_status Dataset {29270/Inf}\n",
      "/gt2l/land_ice_segments/fit_statistics/snr Dataset {29270/Inf}\n",
      "/gt2l/land_ice_segments/fit_statistics/snr_significance Dataset {29270/Inf}\n",
      "/gt2l/land_ice_segments/fit_statistics/w_surface_window_final Dataset {29270/Inf}\n",
      "/gt2l/land_ice_segments/geophysical Group\n",
      "/gt2l/land_ice_segments/geophysical/bckgrd Dataset {29270/Inf}\n",
      "/gt2l/land_ice_segments/geophysical/bsnow_conf Dataset {29270/Inf}\n",
      "/gt2l/land_ice_segments/geophysical/bsnow_h Dataset {29270/Inf}\n",
      "/gt2l/land_ice_segments/geophysical/bsnow_od Dataset {29270/Inf}\n",
      "/gt2l/land_ice_segments/geophysical/cloud_flg_asr Dataset {29270/Inf}\n",
      "/gt2l/land_ice_segments/geophysical/cloud_flg_atm Dataset {29270/Inf}\n",
      "/gt2l/land_ice_segments/geophysical/dac Dataset {29270/Inf}\n",
      "/gt2l/land_ice_segments/geophysical/e_bckgrd Dataset {29270/Inf}\n",
      "/gt2l/land_ice_segments/geophysical/layer_flag Dataset {29270/Inf}\n",
      "/gt2l/land_ice_segments/geophysical/msw_flag Dataset {29270/Inf}\n",
      "/gt2l/land_ice_segments/geophysical/neutat_delay_total Dataset {29270/Inf}\n",
      "/gt2l/land_ice_segments/geophysical/r_eff Dataset {29270/Inf}\n",
      "/gt2l/land_ice_segments/geophysical/solar_azimuth Dataset {29270/Inf}\n",
      "/gt2l/land_ice_segments/geophysical/solar_elevation Dataset {29270/Inf}\n",
      "/gt2l/land_ice_segments/geophysical/tide_earth Dataset {29270/Inf}\n",
      "/gt2l/land_ice_segments/geophysical/tide_equilibrium Dataset {29270/Inf}\n",
      "/gt2l/land_ice_segments/geophysical/tide_load Dataset {29270/Inf}\n",
      "/gt2l/land_ice_segments/geophysical/tide_ocean Dataset {29270/Inf}\n",
      "/gt2l/land_ice_segments/geophysical/tide_pole Dataset {29270/Inf}\n",
      "/gt2l/land_ice_segments/ground_track Group\n",
      "/gt2l/land_ice_segments/ground_track/ref_azimuth Dataset {29270/Inf}\n",
      "/gt2l/land_ice_segments/ground_track/ref_coelv Dataset {29270/Inf}\n",
      "/gt2l/land_ice_segments/ground_track/seg_azimuth Dataset {29270/Inf}\n",
      "/gt2l/land_ice_segments/ground_track/sigma_geo_at Dataset {29270/Inf}\n",
      "/gt2l/land_ice_segments/ground_track/sigma_geo_r Dataset {29270/Inf}\n",
      "/gt2l/land_ice_segments/ground_track/sigma_geo_xt Dataset {29270/Inf}\n",
      "/gt2l/land_ice_segments/ground_track/x_atc Dataset {29270/Inf}\n",
      "/gt2l/land_ice_segments/ground_track/y_atc Dataset {29270/Inf}\n",
      "/gt2l/land_ice_segments/h_li Dataset {29270/Inf}\n",
      "/gt2l/land_ice_segments/h_li_sigma Dataset {29270/Inf}\n",
      "/gt2l/land_ice_segments/latitude Dataset {29270/Inf}\n",
      "/gt2l/land_ice_segments/longitude Dataset {29270/Inf}\n",
      "/gt2l/land_ice_segments/segment_id Dataset {29270/Inf}\n",
      "/gt2l/land_ice_segments/sigma_geo_h Dataset {29270/Inf}\n",
      "/gt2l/residual_histogram Group\n",
      "/gt2l/residual_histogram/bckgrd_per_m Dataset {2759/Inf}\n",
      "/gt2l/residual_histogram/bin_top_h Dataset {748}\n",
      "/gt2l/residual_histogram/count Dataset {2759/Inf, 748}\n",
      "/gt2l/residual_histogram/delta_time Dataset {2759/Inf}\n",
      "/gt2l/residual_histogram/ds_segment_id Dataset {10}\n",
      "/gt2l/residual_histogram/lat_mean Dataset {2759/Inf}\n",
      "/gt2l/residual_histogram/lon_mean Dataset {2759/Inf}\n",
      "/gt2l/residual_histogram/pulse_count Dataset {2759/Inf}\n",
      "/gt2l/residual_histogram/segment_id_list Dataset {2759/Inf, 10}\n",
      "/gt2l/residual_histogram/x_atc_mean Dataset {2759/Inf}\n",
      "/gt2l/segment_quality    Group\n",
      "/gt2l/segment_quality/delta_time Dataset {29270/Inf}\n",
      "/gt2l/segment_quality/record_number Dataset {29270/Inf}\n",
      "/gt2l/segment_quality/reference_pt_lat Dataset {29270/Inf}\n",
      "/gt2l/segment_quality/reference_pt_lon Dataset {29270/Inf}\n",
      "/gt2l/segment_quality/segment_id Dataset {29270/Inf}\n",
      "/gt2l/segment_quality/signal_selection_source Dataset {29270/Inf}\n",
      "/gt2l/segment_quality/signal_selection_status Group\n",
      "/gt2l/segment_quality/signal_selection_status/signal_selection_status_all Dataset {29270/Inf}\n",
      "/gt2l/segment_quality/signal_selection_status/signal_selection_status_backup Dataset {29270/Inf}\n",
      "/gt2l/segment_quality/signal_selection_status/signal_selection_status_confident Dataset {29270/Inf}\n",
      "/gt2r                    Group\n",
      "/gt2r/land_ice_segments  Group\n",
      "/gt2r/land_ice_segments/atl06_quality_summary Dataset {29270/Inf}\n",
      "/gt2r/land_ice_segments/bias_correction Group\n",
      "/gt2r/land_ice_segments/bias_correction/fpb_mean_corr Dataset {29270/Inf}\n",
      "/gt2r/land_ice_segments/bias_correction/fpb_mean_corr_sigma Dataset {29270/Inf}\n",
      "/gt2r/land_ice_segments/bias_correction/fpb_med_corr Dataset {29270/Inf}\n",
      "/gt2r/land_ice_segments/bias_correction/fpb_med_corr_sigma Dataset {29270/Inf}\n",
      "/gt2r/land_ice_segments/bias_correction/fpb_n_corr Dataset {29270/Inf}\n",
      "/gt2r/land_ice_segments/bias_correction/med_r_fit Dataset {29270/Inf}\n",
      "/gt2r/land_ice_segments/bias_correction/tx_mean_corr Dataset {29270/Inf}\n",
      "/gt2r/land_ice_segments/bias_correction/tx_med_corr Dataset {29270/Inf}\n",
      "/gt2r/land_ice_segments/delta_time Dataset {29270/Inf}\n",
      "/gt2r/land_ice_segments/dem Group\n",
      "/gt2r/land_ice_segments/dem/dem_flag Dataset {29270/Inf}\n",
      "/gt2r/land_ice_segments/dem/dem_h Dataset {29270/Inf}\n",
      "/gt2r/land_ice_segments/dem/geoid_h Dataset {29270/Inf}\n",
      "/gt2r/land_ice_segments/fit_statistics Group\n",
      "/gt2r/land_ice_segments/fit_statistics/dh_fit_dx Dataset {29270/Inf}\n",
      "/gt2r/land_ice_segments/fit_statistics/dh_fit_dx_sigma Dataset {29270/Inf}\n",
      "/gt2r/land_ice_segments/fit_statistics/dh_fit_dy Dataset {29270/Inf}\n",
      "/gt2r/land_ice_segments/fit_statistics/h_expected_rms Dataset {29270/Inf}\n",
      "/gt2r/land_ice_segments/fit_statistics/h_mean Dataset {29270/Inf}\n",
      "/gt2r/land_ice_segments/fit_statistics/h_rms_misfit Dataset {29270/Inf}\n",
      "/gt2r/land_ice_segments/fit_statistics/h_robust_sprd Dataset {29270/Inf}\n",
      "/gt2r/land_ice_segments/fit_statistics/n_fit_photons Dataset {29270/Inf}\n",
      "/gt2r/land_ice_segments/fit_statistics/n_seg_pulses Dataset {29270/Inf}\n",
      "/gt2r/land_ice_segments/fit_statistics/sigma_h_mean Dataset {29270/Inf}\n",
      "/gt2r/land_ice_segments/fit_statistics/signal_selection_source Dataset {29270/Inf}\n",
      "/gt2r/land_ice_segments/fit_statistics/signal_selection_source_status Dataset {29270/Inf}\n",
      "/gt2r/land_ice_segments/fit_statistics/snr Dataset {29270/Inf}\n",
      "/gt2r/land_ice_segments/fit_statistics/snr_significance Dataset {29270/Inf}\n",
      "/gt2r/land_ice_segments/fit_statistics/w_surface_window_final Dataset {29270/Inf}\n",
      "/gt2r/land_ice_segments/geophysical Group\n",
      "/gt2r/land_ice_segments/geophysical/bckgrd Dataset {29270/Inf}\n",
      "/gt2r/land_ice_segments/geophysical/bsnow_conf Dataset {29270/Inf}\n",
      "/gt2r/land_ice_segments/geophysical/bsnow_h Dataset {29270/Inf}\n",
      "/gt2r/land_ice_segments/geophysical/bsnow_od Dataset {29270/Inf}\n",
      "/gt2r/land_ice_segments/geophysical/cloud_flg_asr Dataset {29270/Inf}\n",
      "/gt2r/land_ice_segments/geophysical/cloud_flg_atm Dataset {29270/Inf}\n",
      "/gt2r/land_ice_segments/geophysical/dac Dataset {29270/Inf}\n",
      "/gt2r/land_ice_segments/geophysical/e_bckgrd Dataset {29270/Inf}\n",
      "/gt2r/land_ice_segments/geophysical/layer_flag Dataset {29270/Inf}\n",
      "/gt2r/land_ice_segments/geophysical/msw_flag Dataset {29270/Inf}\n",
      "/gt2r/land_ice_segments/geophysical/neutat_delay_total Dataset {29270/Inf}\n",
      "/gt2r/land_ice_segments/geophysical/r_eff Dataset {29270/Inf}\n",
      "/gt2r/land_ice_segments/geophysical/solar_azimuth Dataset {29270/Inf}\n",
      "/gt2r/land_ice_segments/geophysical/solar_elevation Dataset {29270/Inf}\n",
      "/gt2r/land_ice_segments/geophysical/tide_earth Dataset {29270/Inf}\n",
      "/gt2r/land_ice_segments/geophysical/tide_equilibrium Dataset {29270/Inf}\n",
      "/gt2r/land_ice_segments/geophysical/tide_load Dataset {29270/Inf}\n",
      "/gt2r/land_ice_segments/geophysical/tide_ocean Dataset {29270/Inf}\n",
      "/gt2r/land_ice_segments/geophysical/tide_pole Dataset {29270/Inf}\n",
      "/gt2r/land_ice_segments/ground_track Group\n",
      "/gt2r/land_ice_segments/ground_track/ref_azimuth Dataset {29270/Inf}\n",
      "/gt2r/land_ice_segments/ground_track/ref_coelv Dataset {29270/Inf}\n",
      "/gt2r/land_ice_segments/ground_track/seg_azimuth Dataset {29270/Inf}\n",
      "/gt2r/land_ice_segments/ground_track/sigma_geo_at Dataset {29270/Inf}\n",
      "/gt2r/land_ice_segments/ground_track/sigma_geo_r Dataset {29270/Inf}\n",
      "/gt2r/land_ice_segments/ground_track/sigma_geo_xt Dataset {29270/Inf}\n",
      "/gt2r/land_ice_segments/ground_track/x_atc Dataset {29270/Inf}\n",
      "/gt2r/land_ice_segments/ground_track/y_atc Dataset {29270/Inf}\n",
      "/gt2r/land_ice_segments/h_li Dataset {29270/Inf}\n",
      "/gt2r/land_ice_segments/h_li_sigma Dataset {29270/Inf}\n",
      "/gt2r/land_ice_segments/latitude Dataset {29270/Inf}\n",
      "/gt2r/land_ice_segments/longitude Dataset {29270/Inf}\n",
      "/gt2r/land_ice_segments/segment_id Dataset {29270/Inf}\n",
      "/gt2r/land_ice_segments/sigma_geo_h Dataset {29270/Inf}\n",
      "/gt2r/residual_histogram Group\n",
      "/gt2r/residual_histogram/bckgrd_per_m Dataset {2759/Inf}\n",
      "/gt2r/residual_histogram/bin_top_h Dataset {748}\n",
      "/gt2r/residual_histogram/count Dataset {2759/Inf, 748}\n",
      "/gt2r/residual_histogram/delta_time Dataset {2759/Inf}\n",
      "/gt2r/residual_histogram/ds_segment_id Dataset {10}\n",
      "/gt2r/residual_histogram/lat_mean Dataset {2759/Inf}\n",
      "/gt2r/residual_histogram/lon_mean Dataset {2759/Inf}\n",
      "/gt2r/residual_histogram/pulse_count Dataset {2759/Inf}\n",
      "/gt2r/residual_histogram/segment_id_list Dataset {2759/Inf, 10}\n",
      "/gt2r/residual_histogram/x_atc_mean Dataset {2759/Inf}\n",
      "/gt2r/segment_quality    Group\n",
      "/gt2r/segment_quality/delta_time Dataset {29270/Inf}\n",
      "/gt2r/segment_quality/record_number Dataset {29270/Inf}\n",
      "/gt2r/segment_quality/reference_pt_lat Dataset {29270/Inf}\n",
      "/gt2r/segment_quality/reference_pt_lon Dataset {29270/Inf}\n",
      "/gt2r/segment_quality/segment_id Dataset {29270/Inf}\n",
      "/gt2r/segment_quality/signal_selection_source Dataset {29270/Inf}\n",
      "/gt2r/segment_quality/signal_selection_status Group\n",
      "/gt2r/segment_quality/signal_selection_status/signal_selection_status_all Dataset {29270/Inf}\n",
      "/gt2r/segment_quality/signal_selection_status/signal_selection_status_backup Dataset {29270/Inf}\n",
      "/gt2r/segment_quality/signal_selection_status/signal_selection_status_confident Dataset {29270/Inf}\n",
      "/gt3l                    Group\n",
      "/gt3l/land_ice_segments  Group\n",
      "/gt3l/land_ice_segments/atl06_quality_summary Dataset {31678/Inf}\n",
      "/gt3l/land_ice_segments/bias_correction Group\n",
      "/gt3l/land_ice_segments/bias_correction/fpb_mean_corr Dataset {31678/Inf}\n",
      "/gt3l/land_ice_segments/bias_correction/fpb_mean_corr_sigma Dataset {31678/Inf}\n",
      "/gt3l/land_ice_segments/bias_correction/fpb_med_corr Dataset {31678/Inf}\n",
      "/gt3l/land_ice_segments/bias_correction/fpb_med_corr_sigma Dataset {31678/Inf}\n",
      "/gt3l/land_ice_segments/bias_correction/fpb_n_corr Dataset {31678/Inf}\n",
      "/gt3l/land_ice_segments/bias_correction/med_r_fit Dataset {31678/Inf}\n",
      "/gt3l/land_ice_segments/bias_correction/tx_mean_corr Dataset {31678/Inf}\n",
      "/gt3l/land_ice_segments/bias_correction/tx_med_corr Dataset {31678/Inf}\n",
      "/gt3l/land_ice_segments/delta_time Dataset {31678/Inf}\n",
      "/gt3l/land_ice_segments/dem Group\n",
      "/gt3l/land_ice_segments/dem/dem_flag Dataset {31678/Inf}\n",
      "/gt3l/land_ice_segments/dem/dem_h Dataset {31678/Inf}\n",
      "/gt3l/land_ice_segments/dem/geoid_h Dataset {31678/Inf}\n",
      "/gt3l/land_ice_segments/fit_statistics Group\n",
      "/gt3l/land_ice_segments/fit_statistics/dh_fit_dx Dataset {31678/Inf}\n",
      "/gt3l/land_ice_segments/fit_statistics/dh_fit_dx_sigma Dataset {31678/Inf}\n",
      "/gt3l/land_ice_segments/fit_statistics/dh_fit_dy Dataset {31678/Inf}\n",
      "/gt3l/land_ice_segments/fit_statistics/h_expected_rms Dataset {31678/Inf}\n",
      "/gt3l/land_ice_segments/fit_statistics/h_mean Dataset {31678/Inf}\n",
      "/gt3l/land_ice_segments/fit_statistics/h_rms_misfit Dataset {31678/Inf}\n",
      "/gt3l/land_ice_segments/fit_statistics/h_robust_sprd Dataset {31678/Inf}\n",
      "/gt3l/land_ice_segments/fit_statistics/n_fit_photons Dataset {31678/Inf}\n",
      "/gt3l/land_ice_segments/fit_statistics/n_seg_pulses Dataset {31678/Inf}\n",
      "/gt3l/land_ice_segments/fit_statistics/sigma_h_mean Dataset {31678/Inf}\n",
      "/gt3l/land_ice_segments/fit_statistics/signal_selection_source Dataset {31678/Inf}\n",
      "/gt3l/land_ice_segments/fit_statistics/signal_selection_source_status Dataset {31678/Inf}\n",
      "/gt3l/land_ice_segments/fit_statistics/snr Dataset {31678/Inf}\n",
      "/gt3l/land_ice_segments/fit_statistics/snr_significance Dataset {31678/Inf}\n",
      "/gt3l/land_ice_segments/fit_statistics/w_surface_window_final Dataset {31678/Inf}\n",
      "/gt3l/land_ice_segments/geophysical Group\n",
      "/gt3l/land_ice_segments/geophysical/bckgrd Dataset {31678/Inf}\n",
      "/gt3l/land_ice_segments/geophysical/bsnow_conf Dataset {31678/Inf}\n",
      "/gt3l/land_ice_segments/geophysical/bsnow_h Dataset {31678/Inf}\n",
      "/gt3l/land_ice_segments/geophysical/bsnow_od Dataset {31678/Inf}\n",
      "/gt3l/land_ice_segments/geophysical/cloud_flg_asr Dataset {31678/Inf}\n",
      "/gt3l/land_ice_segments/geophysical/cloud_flg_atm Dataset {31678/Inf}\n",
      "/gt3l/land_ice_segments/geophysical/dac Dataset {31678/Inf}\n",
      "/gt3l/land_ice_segments/geophysical/e_bckgrd Dataset {31678/Inf}\n",
      "/gt3l/land_ice_segments/geophysical/layer_flag Dataset {31678/Inf}\n",
      "/gt3l/land_ice_segments/geophysical/msw_flag Dataset {31678/Inf}\n",
      "/gt3l/land_ice_segments/geophysical/neutat_delay_total Dataset {31678/Inf}\n",
      "/gt3l/land_ice_segments/geophysical/r_eff Dataset {31678/Inf}\n",
      "/gt3l/land_ice_segments/geophysical/solar_azimuth Dataset {31678/Inf}\n",
      "/gt3l/land_ice_segments/geophysical/solar_elevation Dataset {31678/Inf}\n",
      "/gt3l/land_ice_segments/geophysical/tide_earth Dataset {31678/Inf}\n",
      "/gt3l/land_ice_segments/geophysical/tide_equilibrium Dataset {31678/Inf}\n",
      "/gt3l/land_ice_segments/geophysical/tide_load Dataset {31678/Inf}\n",
      "/gt3l/land_ice_segments/geophysical/tide_ocean Dataset {31678/Inf}\n",
      "/gt3l/land_ice_segments/geophysical/tide_pole Dataset {31678/Inf}\n",
      "/gt3l/land_ice_segments/ground_track Group\n",
      "/gt3l/land_ice_segments/ground_track/ref_azimuth Dataset {31678/Inf}\n",
      "/gt3l/land_ice_segments/ground_track/ref_coelv Dataset {31678/Inf}\n",
      "/gt3l/land_ice_segments/ground_track/seg_azimuth Dataset {31678/Inf}\n",
      "/gt3l/land_ice_segments/ground_track/sigma_geo_at Dataset {31678/Inf}\n",
      "/gt3l/land_ice_segments/ground_track/sigma_geo_r Dataset {31678/Inf}\n",
      "/gt3l/land_ice_segments/ground_track/sigma_geo_xt Dataset {31678/Inf}\n",
      "/gt3l/land_ice_segments/ground_track/x_atc Dataset {31678/Inf}\n",
      "/gt3l/land_ice_segments/ground_track/y_atc Dataset {31678/Inf}\n",
      "/gt3l/land_ice_segments/h_li Dataset {31678/Inf}\n",
      "/gt3l/land_ice_segments/h_li_sigma Dataset {31678/Inf}\n",
      "/gt3l/land_ice_segments/latitude Dataset {31678/Inf}\n",
      "/gt3l/land_ice_segments/longitude Dataset {31678/Inf}\n",
      "/gt3l/land_ice_segments/segment_id Dataset {31678/Inf}\n",
      "/gt3l/land_ice_segments/sigma_geo_h Dataset {31678/Inf}\n",
      "/gt3l/residual_histogram Group\n",
      "/gt3l/residual_histogram/bckgrd_per_m Dataset {2964/Inf}\n",
      "/gt3l/residual_histogram/bin_top_h Dataset {748}\n",
      "/gt3l/residual_histogram/count Dataset {2964/Inf, 748}\n",
      "/gt3l/residual_histogram/delta_time Dataset {2964/Inf}\n",
      "/gt3l/residual_histogram/ds_segment_id Dataset {10}\n",
      "/gt3l/residual_histogram/lat_mean Dataset {2964/Inf}\n",
      "/gt3l/residual_histogram/lon_mean Dataset {2964/Inf}\n",
      "/gt3l/residual_histogram/pulse_count Dataset {2964/Inf}\n",
      "/gt3l/residual_histogram/segment_id_list Dataset {2964/Inf, 10}\n",
      "/gt3l/residual_histogram/x_atc_mean Dataset {2964/Inf}\n",
      "/gt3l/segment_quality    Group\n",
      "/gt3l/segment_quality/delta_time Dataset {31678/Inf}\n",
      "/gt3l/segment_quality/record_number Dataset {31678/Inf}\n",
      "/gt3l/segment_quality/reference_pt_lat Dataset {31678/Inf}\n",
      "/gt3l/segment_quality/reference_pt_lon Dataset {31678/Inf}\n",
      "/gt3l/segment_quality/segment_id Dataset {31678/Inf}\n",
      "/gt3l/segment_quality/signal_selection_source Dataset {31678/Inf}\n",
      "/gt3l/segment_quality/signal_selection_status Group\n",
      "/gt3l/segment_quality/signal_selection_status/signal_selection_status_all Dataset {31678/Inf}\n",
      "/gt3l/segment_quality/signal_selection_status/signal_selection_status_backup Dataset {31678/Inf}\n",
      "/gt3l/segment_quality/signal_selection_status/signal_selection_status_confident Dataset {31678/Inf}\n",
      "/gt3r                    Group\n",
      "/gt3r/land_ice_segments  Group\n",
      "/gt3r/land_ice_segments/atl06_quality_summary Dataset {31678/Inf}\n",
      "/gt3r/land_ice_segments/bias_correction Group\n",
      "/gt3r/land_ice_segments/bias_correction/fpb_mean_corr Dataset {31678/Inf}\n",
      "/gt3r/land_ice_segments/bias_correction/fpb_mean_corr_sigma Dataset {31678/Inf}\n",
      "/gt3r/land_ice_segments/bias_correction/fpb_med_corr Dataset {31678/Inf}\n",
      "/gt3r/land_ice_segments/bias_correction/fpb_med_corr_sigma Dataset {31678/Inf}\n",
      "/gt3r/land_ice_segments/bias_correction/fpb_n_corr Dataset {31678/Inf}\n",
      "/gt3r/land_ice_segments/bias_correction/med_r_fit Dataset {31678/Inf}\n",
      "/gt3r/land_ice_segments/bias_correction/tx_mean_corr Dataset {31678/Inf}\n",
      "/gt3r/land_ice_segments/bias_correction/tx_med_corr Dataset {31678/Inf}\n",
      "/gt3r/land_ice_segments/delta_time Dataset {31678/Inf}\n",
      "/gt3r/land_ice_segments/dem Group\n",
      "/gt3r/land_ice_segments/dem/dem_flag Dataset {31678/Inf}\n",
      "/gt3r/land_ice_segments/dem/dem_h Dataset {31678/Inf}\n",
      "/gt3r/land_ice_segments/dem/geoid_h Dataset {31678/Inf}\n",
      "/gt3r/land_ice_segments/fit_statistics Group\n",
      "/gt3r/land_ice_segments/fit_statistics/dh_fit_dx Dataset {31678/Inf}\n",
      "/gt3r/land_ice_segments/fit_statistics/dh_fit_dx_sigma Dataset {31678/Inf}\n",
      "/gt3r/land_ice_segments/fit_statistics/dh_fit_dy Dataset {31678/Inf}\n",
      "/gt3r/land_ice_segments/fit_statistics/h_expected_rms Dataset {31678/Inf}\n",
      "/gt3r/land_ice_segments/fit_statistics/h_mean Dataset {31678/Inf}\n",
      "/gt3r/land_ice_segments/fit_statistics/h_rms_misfit Dataset {31678/Inf}\n",
      "/gt3r/land_ice_segments/fit_statistics/h_robust_sprd Dataset {31678/Inf}\n",
      "/gt3r/land_ice_segments/fit_statistics/n_fit_photons Dataset {31678/Inf}\n",
      "/gt3r/land_ice_segments/fit_statistics/n_seg_pulses Dataset {31678/Inf}\n",
      "/gt3r/land_ice_segments/fit_statistics/sigma_h_mean Dataset {31678/Inf}\n",
      "/gt3r/land_ice_segments/fit_statistics/signal_selection_source Dataset {31678/Inf}\n",
      "/gt3r/land_ice_segments/fit_statistics/signal_selection_source_status Dataset {31678/Inf}\n",
      "/gt3r/land_ice_segments/fit_statistics/snr Dataset {31678/Inf}\n",
      "/gt3r/land_ice_segments/fit_statistics/snr_significance Dataset {31678/Inf}\n",
      "/gt3r/land_ice_segments/fit_statistics/w_surface_window_final Dataset {31678/Inf}\n",
      "/gt3r/land_ice_segments/geophysical Group\n",
      "/gt3r/land_ice_segments/geophysical/bckgrd Dataset {31678/Inf}\n",
      "/gt3r/land_ice_segments/geophysical/bsnow_conf Dataset {31678/Inf}\n",
      "/gt3r/land_ice_segments/geophysical/bsnow_h Dataset {31678/Inf}\n",
      "/gt3r/land_ice_segments/geophysical/bsnow_od Dataset {31678/Inf}\n",
      "/gt3r/land_ice_segments/geophysical/cloud_flg_asr Dataset {31678/Inf}\n",
      "/gt3r/land_ice_segments/geophysical/cloud_flg_atm Dataset {31678/Inf}\n",
      "/gt3r/land_ice_segments/geophysical/dac Dataset {31678/Inf}\n",
      "/gt3r/land_ice_segments/geophysical/e_bckgrd Dataset {31678/Inf}\n",
      "/gt3r/land_ice_segments/geophysical/layer_flag Dataset {31678/Inf}\n",
      "/gt3r/land_ice_segments/geophysical/msw_flag Dataset {31678/Inf}\n",
      "/gt3r/land_ice_segments/geophysical/neutat_delay_total Dataset {31678/Inf}\n",
      "/gt3r/land_ice_segments/geophysical/r_eff Dataset {31678/Inf}\n",
      "/gt3r/land_ice_segments/geophysical/solar_azimuth Dataset {31678/Inf}\n",
      "/gt3r/land_ice_segments/geophysical/solar_elevation Dataset {31678/Inf}\n",
      "/gt3r/land_ice_segments/geophysical/tide_earth Dataset {31678/Inf}\n",
      "/gt3r/land_ice_segments/geophysical/tide_equilibrium Dataset {31678/Inf}\n",
      "/gt3r/land_ice_segments/geophysical/tide_load Dataset {31678/Inf}\n",
      "/gt3r/land_ice_segments/geophysical/tide_ocean Dataset {31678/Inf}\n",
      "/gt3r/land_ice_segments/geophysical/tide_pole Dataset {31678/Inf}\n",
      "/gt3r/land_ice_segments/ground_track Group\n",
      "/gt3r/land_ice_segments/ground_track/ref_azimuth Dataset {31678/Inf}\n",
      "/gt3r/land_ice_segments/ground_track/ref_coelv Dataset {31678/Inf}\n",
      "/gt3r/land_ice_segments/ground_track/seg_azimuth Dataset {31678/Inf}\n",
      "/gt3r/land_ice_segments/ground_track/sigma_geo_at Dataset {31678/Inf}\n",
      "/gt3r/land_ice_segments/ground_track/sigma_geo_r Dataset {31678/Inf}\n",
      "/gt3r/land_ice_segments/ground_track/sigma_geo_xt Dataset {31678/Inf}\n",
      "/gt3r/land_ice_segments/ground_track/x_atc Dataset {31678/Inf}\n",
      "/gt3r/land_ice_segments/ground_track/y_atc Dataset {31678/Inf}\n",
      "/gt3r/land_ice_segments/h_li Dataset {31678/Inf}\n",
      "/gt3r/land_ice_segments/h_li_sigma Dataset {31678/Inf}\n",
      "/gt3r/land_ice_segments/latitude Dataset {31678/Inf}\n",
      "/gt3r/land_ice_segments/longitude Dataset {31678/Inf}\n",
      "/gt3r/land_ice_segments/segment_id Dataset {31678/Inf}\n",
      "/gt3r/land_ice_segments/sigma_geo_h Dataset {31678/Inf}\n",
      "/gt3r/residual_histogram Group\n",
      "/gt3r/residual_histogram/bckgrd_per_m Dataset {2964/Inf}\n",
      "/gt3r/residual_histogram/bin_top_h Dataset {748}\n",
      "/gt3r/residual_histogram/count Dataset {2964/Inf, 748}\n",
      "/gt3r/residual_histogram/delta_time Dataset {2964/Inf}\n",
      "/gt3r/residual_histogram/ds_segment_id Dataset {10}\n",
      "/gt3r/residual_histogram/lat_mean Dataset {2964/Inf}\n",
      "/gt3r/residual_histogram/lon_mean Dataset {2964/Inf}\n",
      "/gt3r/residual_histogram/pulse_count Dataset {2964/Inf}\n",
      "/gt3r/residual_histogram/segment_id_list Dataset {2964/Inf, 10}\n",
      "/gt3r/residual_histogram/x_atc_mean Dataset {2964/Inf}\n",
      "/gt3r/segment_quality    Group\n",
      "/gt3r/segment_quality/delta_time Dataset {31678/Inf}\n",
      "/gt3r/segment_quality/record_number Dataset {31678/Inf}\n",
      "/gt3r/segment_quality/reference_pt_lat Dataset {31678/Inf}\n",
      "/gt3r/segment_quality/reference_pt_lon Dataset {31678/Inf}\n",
      "/gt3r/segment_quality/segment_id Dataset {31678/Inf}\n",
      "/gt3r/segment_quality/signal_selection_source Dataset {31678/Inf}\n",
      "/gt3r/segment_quality/signal_selection_status Group\n",
      "/gt3r/segment_quality/signal_selection_status/signal_selection_status_all Dataset {31678/Inf}\n",
      "/gt3r/segment_quality/signal_selection_status/signal_selection_status_backup Dataset {31678/Inf}\n",
      "/gt3r/segment_quality/signal_selection_status/signal_selection_status_confident Dataset {31678/Inf}\n",
      "/orbit_info              Group\n",
      "/orbit_info/crossing_time Dataset {1/Inf}\n",
      "/orbit_info/cycle_number Dataset {1/Inf}\n",
      "/orbit_info/lan          Dataset {1/Inf}\n",
      "/orbit_info/orbit_number Dataset {1/Inf}\n",
      "/orbit_info/rgt          Dataset {1/Inf}\n",
      "/orbit_info/sc_orient    Dataset {1/Inf}\n",
      "/orbit_info/sc_orient_time Dataset {1/Inf}\n",
      "/quality_assessment      Group\n",
      "/quality_assessment/gt1l Group\n",
      "/quality_assessment/gt1l/delta_time Dataset {113/Inf}\n",
      "/quality_assessment/gt1l/lat_mean Dataset {113/Inf}\n",
      "/quality_assessment/gt1l/lon_mean Dataset {113/Inf}\n",
      "/quality_assessment/gt1l/signal_selection_source_fraction_0 Dataset {113/Inf}\n",
      "/quality_assessment/gt1l/signal_selection_source_fraction_1 Dataset {113/Inf}\n",
      "/quality_assessment/gt1l/signal_selection_source_fraction_2 Dataset {113/Inf}\n",
      "/quality_assessment/gt1l/signal_selection_source_fraction_3 Dataset {113/Inf}\n",
      "/quality_assessment/gt1r Group\n",
      "/quality_assessment/gt1r/delta_time Dataset {113/Inf}\n",
      "/quality_assessment/gt1r/lat_mean Dataset {113/Inf}\n",
      "/quality_assessment/gt1r/lon_mean Dataset {113/Inf}\n",
      "/quality_assessment/gt1r/signal_selection_source_fraction_0 Dataset {113/Inf}\n",
      "/quality_assessment/gt1r/signal_selection_source_fraction_1 Dataset {113/Inf}\n",
      "/quality_assessment/gt1r/signal_selection_source_fraction_2 Dataset {113/Inf}\n",
      "/quality_assessment/gt1r/signal_selection_source_fraction_3 Dataset {113/Inf}\n",
      "/quality_assessment/gt2l Group\n",
      "/quality_assessment/gt2l/delta_time Dataset {113/Inf}\n",
      "/quality_assessment/gt2l/lat_mean Dataset {113/Inf}\n",
      "/quality_assessment/gt2l/lon_mean Dataset {113/Inf}\n",
      "/quality_assessment/gt2l/signal_selection_source_fraction_0 Dataset {113/Inf}\n",
      "/quality_assessment/gt2l/signal_selection_source_fraction_1 Dataset {113/Inf}\n",
      "/quality_assessment/gt2l/signal_selection_source_fraction_2 Dataset {113/Inf}\n",
      "/quality_assessment/gt2l/signal_selection_source_fraction_3 Dataset {113/Inf}\n",
      "/quality_assessment/gt2r Group\n",
      "/quality_assessment/gt2r/delta_time Dataset {113/Inf}\n",
      "/quality_assessment/gt2r/lat_mean Dataset {113/Inf}\n",
      "/quality_assessment/gt2r/lon_mean Dataset {113/Inf}\n",
      "/quality_assessment/gt2r/signal_selection_source_fraction_0 Dataset {113/Inf}\n",
      "/quality_assessment/gt2r/signal_selection_source_fraction_1 Dataset {113/Inf}\n",
      "/quality_assessment/gt2r/signal_selection_source_fraction_2 Dataset {113/Inf}\n",
      "/quality_assessment/gt2r/signal_selection_source_fraction_3 Dataset {113/Inf}\n",
      "/quality_assessment/gt3l Group\n",
      "/quality_assessment/gt3l/delta_time Dataset {113/Inf}\n",
      "/quality_assessment/gt3l/lat_mean Dataset {113/Inf}\n",
      "/quality_assessment/gt3l/lon_mean Dataset {113/Inf}\n",
      "/quality_assessment/gt3l/signal_selection_source_fraction_0 Dataset {113/Inf}\n",
      "/quality_assessment/gt3l/signal_selection_source_fraction_1 Dataset {113/Inf}\n",
      "/quality_assessment/gt3l/signal_selection_source_fraction_2 Dataset {113/Inf}\n",
      "/quality_assessment/gt3l/signal_selection_source_fraction_3 Dataset {113/Inf}\n",
      "/quality_assessment/gt3r Group\n",
      "/quality_assessment/gt3r/delta_time Dataset {113/Inf}\n",
      "/quality_assessment/gt3r/lat_mean Dataset {113/Inf}\n",
      "/quality_assessment/gt3r/lon_mean Dataset {113/Inf}\n",
      "/quality_assessment/gt3r/signal_selection_source_fraction_0 Dataset {113/Inf}\n",
      "/quality_assessment/gt3r/signal_selection_source_fraction_1 Dataset {113/Inf}\n",
      "/quality_assessment/gt3r/signal_selection_source_fraction_2 Dataset {113/Inf}\n",
      "/quality_assessment/gt3r/signal_selection_source_fraction_3 Dataset {113/Inf}\n",
      "/quality_assessment/qa_granule_fail_reason Dataset {1}\n",
      "/quality_assessment/qa_granule_pass_fail Dataset {1}\n"
     ]
    }
   ],
   "source": [
    "!h5ls -r {files[0]} "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyproj\n",
    "from astropy.time import Time\n",
    "\n",
    "def gps2dyr(time):\n",
    "    \"\"\"Converte GPS time to decimal years.\"\"\"\n",
    "    return Time(time, format='gps').decimalyear\n",
    "\n",
    "\n",
    "def orbit_type(time, lat, tmax=1):\n",
    "    \"\"\"Separate tracks into ascending and descending.\n",
    "    \n",
    "    Defines tracks as segments with time breaks > tmax,\n",
    "    and tests whether lat increases or decreases w/time.\n",
    "    \"\"\"\n",
    "    tracks = np.zeros(lat.shape)  # generate track segment\n",
    "    tracks[0:np.argmax(np.abs(lat))] = 1  # set values for segment\n",
    "    is_asc = np.zeros(tracks.shape, dtype=bool)  # output index array\n",
    "\n",
    "    # Loop trough individual secments\n",
    "    for track in np.unique(tracks):\n",
    "    \n",
    "        i_track, = np.where(track == tracks)  # get all pts from seg\n",
    "    \n",
    "        if len(i_track) < 2: continue\n",
    "    \n",
    "        # Test if lat increases (asc) or decreases (des) w/time\n",
    "        i_min = time[i_track].argmin()\n",
    "        i_max = time[i_track].argmax()\n",
    "        lat_diff = lat[i_track][i_max] - lat[i_track][i_min]\n",
    "    \n",
    "        # Determine track type\n",
    "        if lat_diff > 0:  is_asc[i_track] = True\n",
    "    \n",
    "    return is_asc\n",
    "\n",
    "\n",
    "def transform_coord(proj1, proj2, x, y):\n",
    "    \"\"\"Transform coordinates from proj1 to proj2 (EPSG num).\n",
    "\n",
    "    Example EPSG projections:\n",
    "        Geodetic (lon/lat): 4326\n",
    "        Polar Stereo AnIS (x/y): 3031\n",
    "        Polar Stereo GrIS (x/y): 3413\n",
    "    \"\"\"\n",
    "    # Set full EPSG projection strings\n",
    "    proj1 = pyproj.Proj(\"+init=EPSG:\"+str(proj1))\n",
    "    proj2 = pyproj.Proj(\"+init=EPSG:\"+str(proj2))\n",
    "    return pyproj.transform(proj1, proj2, x, y)  # convert\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import h5py\n",
    "import numpy as np\n",
    "\n",
    "def read_atl06(fname, outdir='data', bbox=None):\n",
    "    \"\"\"Read one ATL06 file and output 6 reduced files. \n",
    "    \n",
    "    Extract variables of interest and separate the ATL06 file \n",
    "    into each beam (ground track) and ascending/descending orbits.\n",
    "    \"\"\"\n",
    "\n",
    "    # Each beam is a group\n",
    "    group = ['/gt1l', '/gt1r', '/gt2l', '/gt2r', '/gt3l', '/gt3r']\n",
    "\n",
    "    # Loop trough beams\n",
    "    for k, g in enumerate(group):\n",
    "    \n",
    "        #-----------------------------------#\n",
    "        # 1) Read in data for a single beam #\n",
    "        #-----------------------------------#\n",
    "        \n",
    "        data = {}\n",
    "    \n",
    "        try:\n",
    "            # Load vars into memory (include as many as you want)\n",
    "            with h5py.File(fname, 'r') as fi:\n",
    "                \n",
    "                data['lat'] = fi[g+'/land_ice_segments/latitude'][:]\n",
    "                data['lon'] = fi[g+'/land_ice_segments/longitude'][:]\n",
    "                data['h_li'] = fi[g+'/land_ice_segments/h_li'][:]\n",
    "                data['s_li'] = fi[g+'/land_ice_segments/h_li_sigma'][:]\n",
    "                data['t_dt'] = fi[g+'/land_ice_segments/delta_time'][:]\n",
    "                data['q_flag'] = fi[g+'/land_ice_segments/atl06_quality_summary'][:]\n",
    "                data['s_fg'] = fi[g+'/land_ice_segments/fit_statistics/signal_selection_source'][:]\n",
    "                data['snr'] = fi[g+'/land_ice_segments/fit_statistics/snr_significance'][:]\n",
    "                data['h_rb'] = fi[g+'/land_ice_segments/fit_statistics/h_robust_sprd'][:]\n",
    "                data['dac'] = fi[g+'/land_ice_segments/geophysical/dac'][:]\n",
    "                data['f_sn'] = fi[g+'/land_ice_segments/geophysical/bsnow_conf'][:]\n",
    "                data['dh_fit_dx'] = fi[g+'/land_ice_segments/fit_statistics/dh_fit_dx'][:]\n",
    "                data['tide_earth'] = fi[g+'/land_ice_segments/geophysical/tide_earth'][:]\n",
    "                data['tide_load'] = fi[g+'/land_ice_segments/geophysical/tide_load'][:]\n",
    "                data['tide_ocean'] = fi[g+'/land_ice_segments/geophysical/tide_ocean'][:]\n",
    "                data['tide_pole'] = fi[g+'/land_ice_segments/geophysical/tide_pole'][:]\n",
    "                \n",
    "                rgt = fi['/orbit_info/rgt'][:]                           # single value\n",
    "                t_ref = fi['/ancillary_data/atlas_sdp_gps_epoch'][:]     # single value\n",
    "                beam_type = fi[g].attrs[\"atlas_beam_type\"].decode()      # strong/weak (str)\n",
    "                spot_number = fi[g].attrs[\"atlas_spot_number\"].decode()  # number (str)\n",
    "                \n",
    "        except:\n",
    "            print('skeeping group:', g)\n",
    "            print('in file:', fname)\n",
    "            continue\n",
    "            \n",
    "        #---------------------------------------------#\n",
    "        # 2) Filter data according region and quality #\n",
    "        #---------------------------------------------#\n",
    "        \n",
    "        # Select a region of interest\n",
    "        if bbox:\n",
    "            lonmin, lonmax, latmin, latmax = bbox\n",
    "            bbox_mask = (data['lon'] >= lonmin) & (data['lon'] <= lonmax) & \\\n",
    "                        (data['lat'] >= latmin) & (data['lat'] <= latmax)\n",
    "        else:\n",
    "            bbox_mask = np.ones_like(data['lat'], dtype=bool)  # get all\n",
    "            \n",
    "        # Only keep good data (quality flag + threshold + bbox)\n",
    "        mask = (data['q_flag'] == 0) & (np.abs(data['h_li']) < 10e3) & (bbox_mask == 1)\n",
    "        \n",
    "        # If no data left, skeep\n",
    "        if not any(mask): continue\n",
    "        \n",
    "        # Update data variables\n",
    "        for k, v in data.items(): data[k] = v[mask]\n",
    "            \n",
    "        #----------------------------------------------------#\n",
    "        # 3) Convert time, separate tracks, reproject coords #\n",
    "        #----------------------------------------------------#\n",
    "        \n",
    "        # Time in GPS seconds (secs sinde Jan 5, 1980)\n",
    "        t_gps = t_ref + data['t_dt']\n",
    "\n",
    "        # Time in decimal years\n",
    "        t_year = gps2dyr(t_gps)\n",
    "\n",
    "        # Determine orbit type\n",
    "        is_asc = orbit_type(t_year, data['lat'])\n",
    "        \n",
    "        # Geodetic lon/lat -> Polar Stereo x/y\n",
    "        x, y = transform_coord(4326, 32607, data['lon'], data['lat'])\n",
    "      \n",
    "        data['x'] = x\n",
    "        data['y'] = y\n",
    "        data['t_gps'] = t_gps\n",
    "        data['t_year'] = t_year\n",
    "        data['is_asc'] = is_asc\n",
    "        \n",
    "        #-----------------------#\n",
    "        # 4) Save selected data #\n",
    "        #-----------------------#\n",
    "        \n",
    "        # Define output dir and file\n",
    "        outdir = Path(outdir)    \n",
    "        fname = Path(fname)\n",
    "        outdir.mkdir(exist_ok=True)\n",
    "        outfile = outdir / fname.name.replace('.h5', '_' + g[1:] + '.h5')\n",
    "        \n",
    "        # Save variables\n",
    "        with h5py.File(outfile, 'w') as fo:\n",
    "            for k, v in data.items(): fo[k] = v\n",
    "            print('out ->', outfile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "python: can't open file 'system-status.py': [Errno 2] No such file or directory\n"
     ]
    }
   ],
   "source": [
    "!python system-status.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running in parallel (8 jobs) ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=8)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=8)]: Done   2 tasks      | elapsed:    4.8s\n",
      "[Parallel(n_jobs=8)]: Done  14 out of  24 | elapsed:    8.4s remaining:    6.0s\n",
      "[Parallel(n_jobs=8)]: Done  19 out of  24 | elapsed:   10.7s remaining:    2.8s\n",
      "[Parallel(n_jobs=8)]: Done  24 out of  24 | elapsed:   12.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=8)]: Done  24 out of  24 | elapsed:   12.3s finished\n"
     ]
    }
   ],
   "source": [
    "outdir = Path.home()/'shared/data-Jun/Data1/'\n",
    "\n",
    "njobs = 8\n",
    "\n",
    "bbox = None  #[-1124782, 81623, -919821, -96334]  # Kamb bounding box\n",
    "\n",
    "outdir.mkdir(exist_ok=True)\n",
    "\n",
    "\n",
    "if njobs == 1:\n",
    "    print('running in serial ...')\n",
    "    [read_atl06(f, outdir, bbox) for f in files]\n",
    "\n",
    "else:\n",
    "    print('running in parallel (%d jobs) ...' % njobs)\n",
    "    from joblib import Parallel, delayed\n",
    "    Parallel(n_jobs=njobs, verbose=5)(delayed(read_atl06)(f, outdir, bbox) for f in files)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/jovyan/shared/data-Jun/Data1/ATL06_20191221034540_13020503_003_01_gt2r.h5\n",
      "/home/jovyan/shared/data-Jun/Data1/ATL06_20191221033709_13020502_003_01_gt3l.h5\n",
      "/home/jovyan/shared/data-Jun/Data1/ATL06_20191126045248_09210502_003_01_gt2r.h5\n",
      "/home/jovyan/shared/data-Jun/Data1/ATL06_20191204043607_10430502_003_01_gt2l.h5\n",
      "/home/jovyan/shared/data-Jun/Data1/ATL06_20191127172032_09440505_003_01_gt1r.h5\n",
      "/home/jovyan/shared/data-Jun/Data1/ATL06_20191130044427_09820502_003_01_gt2r.h5\n",
      "/home/jovyan/shared/data-Jun/Data1/ATL06_20191118050929_07990502_003_01_gt2r.h5\n",
      "/home/jovyan/shared/data-Jun/Data1/ATL06_20191004200533_01210506_003_01_gt3l.h5\n",
      "/home/jovyan/shared/data-Jun/Data1/ATL06_20181022000240_03570103_003_01_gt1r.h5\n",
      "/home/jovyan/shared/data-Jun/Data1/ATL06_20191017190928_03190505_003_01_gt1r.h5\n",
      "Total number of files: 144\n"
     ]
    }
   ],
   "source": [
    "#outfiles = !ls {outdir}/*.h5\n",
    "outfiles = list(outdir.glob('*.h5'))\n",
    "\n",
    "for f in outfiles[:10]: print(f)\n",
    "print('Total number of files:', len(outfiles))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/                        Group\n",
      "/dac                     Dataset {32015}\n",
      "/dh_fit_dx               Dataset {32015}\n",
      "/f_sn                    Dataset {32015}\n",
      "/h_li                    Dataset {32015}\n",
      "/h_rb                    Dataset {32015}\n",
      "/is_asc                  Dataset {32015}\n",
      "/lat                     Dataset {32015}\n",
      "/lon                     Dataset {32015}\n",
      "/q_flag                  Dataset {32015}\n",
      "/s_fg                    Dataset {32015}\n",
      "/s_li                    Dataset {32015}\n",
      "/snr                     Dataset {32015}\n",
      "/t_dt                    Dataset {32015}\n",
      "/t_gps                   Dataset {32015}\n",
      "/t_year                  Dataset {32015}\n",
      "/tide_earth              Dataset {32015}\n",
      "/tide_load               Dataset {32015}\n",
      "/tide_ocean              Dataset {32015}\n",
      "/tide_pole               Dataset {32015}\n",
      "/x                       Dataset {32015}\n",
      "/y                       Dataset {32015}\n"
     ]
    }
   ],
   "source": [
    "!h5ls -r {outfiles[0]}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Select the data from a wide vriety of parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of files: 144\n",
      "Number of points: 1912111\n",
      "               x             y         h_li       t_year\n",
      "0  767473.378066  6.607381e+06  1404.785400  2019.970294\n",
      "1  767470.084337  6.607400e+06  1406.027954  2019.970294\n",
      "2  763520.559803  6.629979e+06  1423.722168  2019.970294\n",
      "3  763517.047962  6.629999e+06  1422.662598  2019.970294\n",
      "4  762401.740253  6.636434e+06  1717.192383  2019.970294\n"
     ]
    }
   ],
   "source": [
    "import h5py\n",
    "import numpy as np\n",
    "import dask.dataframe as dd\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "\n",
    "def read_h5(fname, vnames=[]):\n",
    "    \"\"\"Read a list of vars [v1, v2, ..] -> 2D.\"\"\"\n",
    "    with h5py.File(fname, 'r') as f:\n",
    "        return np.column_stack([f[v][()] for v in vnames])\n",
    "\n",
    "    \n",
    "# Get list of files to plot\n",
    "#files = list(Path('/home/jovyan/tutorial-data/gridding-time-series/org').glob('*.h5'))\n",
    "files = list(outdir.glob('*.h5'))\n",
    "\n",
    "# Variables we want to plot\n",
    "#vnames = ['lon', 'lat', 'h_elv']\n",
    "vnames = ['x', 'y', 'h_li','t_year']\n",
    "\n",
    "# List with one dataframe per file\n",
    "dfs = [dd.from_array(read_h5(f, vnames), columns=vnames) for f in files]\n",
    "\n",
    "# Single parallel dataframe (larger than memory)\n",
    "df = dd.concat(dfs)\n",
    "\n",
    "print('Number of files:', len(files))\n",
    "print('Number of points:', len(df))\n",
    "print(df.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save it as csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['/home/jovyan/shared/data-Jun/Data1/points-000.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-001.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-002.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-003.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-004.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-005.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-006.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-007.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-008.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-009.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-010.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-011.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-012.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-013.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-014.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-015.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-016.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-017.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-018.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-019.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-020.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-021.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-022.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-023.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-024.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-025.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-026.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-027.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-028.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-029.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-030.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-031.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-032.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-033.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-034.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-035.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-036.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-037.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-038.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-039.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-040.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-041.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-042.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-043.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-044.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-045.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-046.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-047.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-048.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-049.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-050.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-051.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-052.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-053.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-054.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-055.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-056.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-057.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-058.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-059.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-060.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-061.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-062.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-063.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-064.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-065.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-066.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-067.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-068.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-069.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-070.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-071.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-072.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-073.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-074.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-075.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-076.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-077.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-078.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-079.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-080.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-081.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-082.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-083.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-084.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-085.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-086.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-087.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-088.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-089.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-090.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-091.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-092.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-093.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-094.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-095.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-096.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-097.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-098.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-099.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-100.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-101.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-102.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-103.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-104.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-105.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-106.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-107.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-108.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-109.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-110.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-111.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-112.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-113.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-114.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-115.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-116.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-117.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-118.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-119.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-120.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-121.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-122.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-123.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-124.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-125.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-126.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-127.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-128.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-129.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-130.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-131.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-132.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-133.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-134.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-135.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-136.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-137.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-138.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-139.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-140.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-141.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-142.csv',\n",
       " '/home/jovyan/shared/data-Jun/Data1/points-143.csv']"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.to_csv(str(outdir)+'/points-*.csv')  # -> N csv files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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x6uWbUn+AXLsGoep1GL9vWf/rZOZSnDL0OL2dv4M3bnQpJzbXIsbjZCmW6uWSr2Z1M/hRmSZy27lhRU7IDW6ZBDsxyHLiPgmCHexey255fgg2A5CXJz6KE5naw1NXPoENwSA59uSYB/GNzsJWbcgEe8+doyfgAAAD9gPsfpoc60ok4KsHb3P9C+jLAwJQyaSP9x0fT/e7NKxFiFQB/Mcu1//+VOCa1bk+kVzANTiKl0lxoZoIuGWS4NQJvVuOnyCkA/dOmHxd6n5jgvOwLYXwmwkbcd3xe8mLYEKgBL7+wWpy7GvDEDa2EWpGwnna67bj7MpKuIto9odr1yAM2E/2WByY/BhyE3c38fLER/GNE4w7/MylOL6U+Y1VrztOESxd2rdOwWSlWFyGhGtkzyZmpoN6cSTzMgGQBnoh9xSSAlaucWqAJrifhM/PZs7ffYn3G5OFLPeQlhbb6uF3kxfBjtEP4mtnaZfun457uKgf2FbZMPJ+thTrS4OibLO/FxEWf/Q999+AJxpYZatDPT20Wth1qxEG7OeVxHLAgDs+yvh3n9hcy94zcZ9P2WyYj4b0409ejQmPRgYapooXhdT8FjdOncwWe5AhFBalD7Q78j7ri+p9IApZhUFaAYjK2+NELAbDf7eEHN/86acArxVvL0jTn75j+bdTmaRXL9+Ex6NR+Mp7K/uNfX/sQ3gyFod/PHy7u4/vnEbWHV4/MxFwwAF6u+5RhjdKnjjzwOisTFZ0jwcG3Xaw3zk9sbkWdY8HBq98r99Y+9YpiPG45VKs6sVv65nKQOgeD8QZtTMAOmtjbideJoWLU4OMQied4y7nrLA4d8dQCagFR5BWAPLyxEfZC/nUlU/gZKZP5GjXIBhUkvuG6Zzzo0Pkg3v3ZY9YegD8dNzDeCwaJcdUn1Hp8UA4UQBNvScaaO+TxhWoH/oW7Q5/w/yCk+QFADi5cQJqzDk9uXECYpyWYfZEo8CN9SWTzettO67ChN/Plo5xZWVtO67CWCBABlSCO3BjSVMhZDnyFWQUwrErJqjnrhNRZTk4eV7BXaRth/mlP96qD91Lm+CNKSmhN2pYi39WXp7uVzuHHzM39IIa/ibZc6elh8CeUIjNJFX7fKRk75NjHsQTsZj7FccW1OCXZ6ynx8LVAMEP+G1/lccmtV0RW98dvOcT5XZaJMJmDrhMhxlUWM0qxAKBjDWvn9w4gb3+VWVlbjfQEtTIhFUQMkP5/Z+m/KxWPXudhAQZhU/aAcirl2/C9hDdMPzkmAcRfks3nO/u7GYdsV2Hv4Mf4xy6TzQA/OzFpL8/Om0ntkWjQHlq/PDSdRhiVsVfmLAROy2ufDse6hpqqUYY9WP638+ai3BkXpZ3SkHCy9bZBu/5ROfGQvdcrHzgmg3plas/IMutAGhVrLYdV2FPRYWlXW/fOgVVEr+pwskIAxiBBpXpOLlxAsZ9PlsvV8EZFHqQka+ypHxlKgr9fLoVVXabek8AuCcDYhdOhVFwHhk5UyMrTgGXBQFvCPDN1v5j9dvxVNfwTHx9/ukelfrY7CbLD/QZ5eXABRpBTYNvfEg3nR8864EA48niFl7+YDpAZx09eGQ+wE/20r8v8BnAP4fy89sXa+y5Dd1zMYYrK21/dNzng4QiQ8A543qi0ZRePJlSzzIDI+rzzAwJFYR0rrsUAeyt8Anuw4l9Bk7cp2RIkCGYeJjybU80mjeJ9mwz8usv617OR01wHBkJQI5O3cZ6f1xdGYEarhTr2jWYT8WijBE8xGc6wtV8SU6EkZrtw6uXb8IPe3rIQG7r6Afw3XPnYCRVpjVjM14eCJCSva7hhvkIMfraggU1CEP28NsyTey5QNXol06J0akN41HXNLa3guu7MNPuVnsr2rdOwUz1YZiZG4qTGycgFzQJhYUTJ/TS+yC4GW6yPWT5fl2VHclklttpyH3pHjL65qd6Dt6o3ax06B4/9HAmdyE/nKkFdpIMAKx7efCQ5V6Frqt2oB6+iBx79fJNGGAmcbHP/3M1WgTgF7+gj1P3KDr4a6k2VLScGOAqMiRW6Lp/JLs9xuNsgJNq4JPQNFJ5CyB1uWFOxQsAIO73k/0vZ+8ahlokkrQ3RnAOhTahl30SnIxVgZG+ZCMDkmnhEDuKjLFAQLykXETGApDnatZjG5PyqykpYVfhh3i9mdqF/NFSbbhwUyU/rX6EQBu93ay5CDfwhjr96B7FOn0fjURIh/B9k7ZgOJHgndXdwOwmhMq99FigDSou/RE9NmA/uFERa9BtB3WuftckFgiQQcCJzbUYDQSg4q6jZMmTKqhIhTMPjEYuMBi88j2978vo5MYJGGP268wDo1H3eMC3savfWNf9IzHu84F3c7d7r1/hPE6cPDtxnzJNMfzGYoRTRtQ9HnbBKRYIZCUDohIdyTSqvsKE35+r3RDSJKMZkEmlpeTfD07Zil6kr81XL9+EPpf3KQAAQEJx0c9XqE4wUr0k17ciJPwAv3683zbhaTuR6zc5OGUrnoi5PA8SrqazINe3YlfHZDoLcu0aVJ6XLBNY16Hb6WGI+/1KDw0Ao0dCZ7JeZx4YjdxDWPd4IG7xAX1qw3jUPR4226FFo2Sjoxno9B3run8kW4p19q5h7D0SGTDA9kqfkHtkslscOLGkrtjgylfjPh/7Dkmn5FW1MJaJha3eqDL2XOAluIuMBiC7L3sEJ777bfIirPR4WOPCBVVV8NWDt7n7YdZSrX7pciVEKqleiqYWxMrf02OxICvvG0ok3K061lFvlLpRnGjgm/0/a0wtyMsg4dVVrD+HijMPjEa08IDVPR6lqy0V/HSuuxTjgYDl5u6kgQ7zkji1YTw7xn23v7sbAus6+o3Fl5VgSVeXlGIJjsCpE+9sBH5O/a2CATdJ90SjyiZ0u5jiIBSnNozP6PWnCkBUQZQsVrmHjHd/ljKZjhcmbMQQc2F8f+xDrJysq1gU5G/A5uaM3Zz6Z9fRpVgzlyIXhESn7cQ/Ky93ryrWvDrjt1GeK/Pq+GN7ppbvwckByaR1Oaw4qofuuRgTPj9ZnnT2rmHIrYCpxii67h+JVGAAAKBrGhsYJHy+fgFF6J6LEeNxMqty9q5hiPE4GVSpthOcgx2BBSdOcp24T3YppN8iFA92MjWq0jPBeWQ8AGmLxdiV9lAiwZrqfdjTA197/7vuf1BypoSZ5Ib5CNe30hPU61Yj50uy+7JHsMLNN6fKb0WLADxFTJJb/QhlRwB++axjr610UteYiIMnwq9olaw/zX62aqwv4aogqeZm9mhwQYOuefp9T+ieizEeCJDfo3s87AqdlayQkF84v5nqxW/rCZ+zarPd1jCv2t+RX39Zt/t73HYchC8IttH9paoFpogFE2gneIWoyqy4sYSmpVViJuSWjJ+p41O34WRFL0iYuXAOX/E4HioE/WZVv0eGYbMZkSravA8AvIhks7ormF/L93SoJI3D1WqvljxSufoDPcZMxkvWn9a5zINJeHUVAgB4tvb07wtaXYU9VXTAGV5dhT0DrJkTAsAXgRzBuTuGsr0dAABxf/+XIcbjZObE/D1UmdaZB0ZjwueTUiyHovKOift8ECulr/PIgAGOnMg6cZ9UZGt/3XYcigmu3CgWCLCLAZqFEiWnZxG4360lEtIf4iKyEiqWaxpUvL2YfBkFNI01LRzm88HfF0IWJEeEo6W0zGzDWhxUcZz0DTk+dRuiFgN4ZZU7j3PCD/CDI/33fWEVghahx26sR6ueK7mmc92lGKkYQI71rBpoycE84dHAe44J3gNtALufps/1gAOpZYYSfvbfh1dXIW6L9hvrWTUQAaBfmVjonotR93jIng+zbI0KNEL3XIxc87uQX5I1jXLKO7JiKQipM/LrL+tccOgNh22X/wLkVtGKw85zQTIg7iIrZ2r3ZY+wF+/xqdtwIrPiu/uyR/DN7u5s7FJhMmMzQriaHDp15ROod15BjunhiyybIDoOLcL3dKh6PbRIbsrjbNCzaiByjdnecJiUp+1NdGUFatEIOZmHBTUIx+aSGRKYX4vwSbP1Hb1pFMKZWqACDQAAfeBHZMN/bHkZYjze73fESujnAEDyRkKVG7yQP9q3TiGf/an0HOUSt63uS5ZDsELFxx+ntX2mPT3soMpkOD1DI1gja6Hifx1cBV/6463kRby3u5vtE/kwEgF4rb+hocAQC/IKW7EgbXTYsBbBG2LLtBzNghpkA40FNaxr+qDLt0JgwpYs7pgFKJ+Yz2ElE/1+4Eq0eqN7POy/84b429zTo7MBBcn8WtRLGGPRQBtryKl7PBDtU3vcs2ogYjzOZkEwHud9QzSNHBPyh2pSwI0NWb5fl8mEINjDTv+OFRUsJ2RA7KhZiQ+Iu8haALJj9IOsA3rXVTvYizs6bSdW+AugFyRXNDcbfRE/393/QdTUgmzZTMNax5YlJSV4iC8dYsqwTl35BIZPTcl/M/rTbWwJE/X3+LIS1CuOJ/3Y8OoqjA0Ik789trwM4yVIBkDxZSVGQEE18HNw2aS5DcZv+NmL5D6AFutXinXujqGI8TjZ86HyDVG5wQvOonL1BzpXFpFP1Zq+AbEVRFFKcDtel/TaqhbeuLG4zyclWC4iq2eqPewHeGkd+cA+dXISW5ve1T0km7tVeMxu4puzZ83lMwYJP+us7mi6Rxm+IBSxIF9edqY2v6Vni4II1btp00QVN41CsrelLwP2A9corixdKztibGuVhVWGMz3VR9RSbTSrU9dVq58NGlQvDVXTuR2jRyE7qFZNVUFGvlZbw5WV5N9HtLxSMFmZbAVMEoi5F/+ZM/nehbRx4oKGkDrZDRVnbGZLYiDhB6h6nRzCwGd0c7XAE2ijy60AjAkmdTxNZ/V8ZwVSZW6DcV0RsrCwKIjYU0JvF6rhA5dc0dSC5zMFqXDTqOTbzG0wJJipwH5RECF4iB6bX4vKrBJF8JBxPCliQd40Evordp27YygmfH6y1yV0z8UYCwRIyWBTAjgVOWEhO6hEATAeZw3M8rVaOei2gzpX4qFr/csFe+O0fgkJBooXp12LmURVKiZKV4VB9p/+4Wq6POj6VoSOerJ/QZ++C6GzLuu7VlDcMB/ZfpBr17DN6tDUghCgtcQdTaDNmAQzkH0NN41Cb0hLrech16Tr2t7cjKzk8JA9fBYkUgXsNUJx7Rrj/v331/vv76y5RmBLlGnFlpdhQtP6BSE9qway5VZxnw8SHv5R5e1xR0lBIaNqMnfqiiQ3iekZUMEqdkUG0Ip12SYfXh0S2DifYj1H3OKBygFecB7ZD0AiVcCWB3WPUpfEUJMbgUflth6pImvzAcAoz+GyJ05lfi1bWqYlEryZXTzu7BrY6t3pf0YsCPCTvf3P53WrEc7U0mPNzUaQaqXUy2ReHb/NjfWsIaa+xEdep7rHQ7q6R1dWoCcaJc0OVb4hQu5QBRmqbEI+gxOu9EvV/GpFEMJNiAmhu5FzcCEYj9tqXhfyQ/YDkDmNhj8DlQWZ08j3LqjGBDVUKc3sJmQnpo0rjFXzVHsT8k3HdHLCGl9WghiPs5NZp67IAgDAzKXpv1BUxosqQ0a79xu3iBCpUip/9S25ii8rQW84zJoqhisrAXYk+o2dvWsYqiR9hewTLaODDFX/Tj69XFQlHHaa0/ONTESFQkOayQuf3JxhLWL8x6HKdFATZoGnudlYeaaCkBvr+ZeUnb6EPOOJRtkV1ITfz47pHg/ti5FLfrxfTynbkAo3jUIYsJ8OKBfUIAzZQzeJm30mqZSB3TTKyERRKlqmcz2nvkWcn3BlJcRL6EtR93jYzFakvCypX4qQPUzDSQpuRbJz3aXI9YZkG9UqKReAVNx1VI/mMQOSjyBDAhshX9hZKDyxuVauVxeRmwCkuZmXfFVNiufVubM/Id+YzeUUTG0+ABgr1tmaFGeB+LIS5CarcZ+PnciqxnKGNwRciZJna4+edoAUruYzE+FqPtuh2C+WlmrjeqNEAVr9bKlcbHlZf/f0xZpxYohMByzWUItEyEBDX+JDTzTq7P6eAoUqjTOJBQKsI7MTA8aS9adZyWAs0MZXCTIEtyFlVoVBbnNcXImPN8Sb6ZUdYaV8CxquX8MqlXvpLIi5Kk35UajGnErZETpLtlgzMkHUav5iDTkFtpxxYz3CvDrW+yM+8jfpff6CGoSLf0GbTZpN4tz1ETxkz6SSEwXwhtisiu7T+we9rX7kMh3ecJjNgoQrK43PE3JKwseX7oWrGBVEyF//R+XqD3SuaR4Tcfb3RCoq4MwDo/MyWVcdK1UAIcGF4FZUQYaqhFKa0N1D7gKQuQ28H8WcRrZhFcqO8Mo9hcycxvReHNeuMfo6qGzHgho+Q9JS7aoXlu/yDQAX/4IeHPGs8R9Fwu/s8j6uWTwVYkFe2crfYQSpFEP2pJ4FWRRE34QtMPb3S+nrLVxNB7atfoTuEf3KtOIlSGZBzt0xFHsqKujeksUaYhRFljfH9FSW8oMOfHYnPB6IM27JpSdPsuVkKqWvbJPP7xaEfODoXk0hI+Q2A+Lv4NWWEn6AXz9OO3ZrEQi8dXPhTSq4UqhMcWO92u2cc79O+FNzxs4j0Wk7eTnYmUv5wDbh5037cgklzgBg9FYwGRLLzFxqmAJSmQ6z2Z3KLtZv52WbFUSn7cTDJy6lB00fEgpvd/+/tfoRtBhbbsV9lr7Eh1pU0W8mZB7VfcT0/vk2drE+HNkm4fOBbqPBNZ/qeQkmYBKEQkXKrAqf3AYgN8xHthn9hvlshgT9nRCOuzga5spZ5uegYSoWpFeLF1axDcLnx6iafieS8APbK9Q9ijbfM/uL7JQaZZJsl4I1rEWs/g96zN8BvorD9Fioxp40s7+DXkgAMCajVNC9KGhcb30yHt7uGK9IFK5mr09vOAwD7vjIHdduIfPPIZ01ogUALZ6fngrd44Gu+0em/OxVlZplGycpAhWr94SbyEfpXaavC1UGRJURdNK9IqjJ/ZkK1fCqV92jyN4Hffou3lzNDVy7Jn9lTYuCRmBHNZebEz9uO7egMCQEfwdfTlS5F3zlH2dnn6xy7Rpk+58yhBeRzXTEdB3wzVa6T8SOAETDWkOB65VVtOy2v4O+/1v92HfFK7a8zPgb1ZDeUo2eHp1sOu+6fyTqmgYVdx2ViVK2eapDGWSopJ1VylnZRDVBcaLD8uCV7+ncZGzosn1ZucYlyBDyjd0MiBtltIuV3AcgKrnXcDUv1xup4ldWBTXJ+jpUmQ5q8uc0rm9FqN4N8OKjtAt8Zx27afT0hCzumEVslDulQvTkNOAmiSWIoCe85Fhg4Hsw6J1vpn7+67cbZXEUip4AfYkP+5ak6T6dfRHFBx1lJ4y6xyM1xLlgwH7+mV12xCilI8inoZ+qlIoLTkrWn9YTnvytrKokxTmqF7+dtWe3NLfnH7vnIFvnLtOfq1oMsJsdEZxFfp6oWoRWeZrTaKyeUlzf6u4siBOgsiDmBIEKNFRjTuNEAz+5TfhpBbb67cY1leUMRFJUUtSZ6BO6vtXoBSJ6QcLTdiIXnPToOpw6O9jed4ar6UzH7Ca143qg7cL9NK9BKkheUIPec2Gy6Tx0z8WI8bjSCE/IAAn/F/4xfVF4P+lD38nWHiVF1U9xasN48rf0rBqI0ZUVeZl0RwMB1rVdNRFr23GVcn8ly+FuVOfP7li29scOqgyIaszfTfQUCo4kfwEIl7ZnyrAAgJ/UCMm5aRRfbvV5wy+J6cvgdG6Yz06kAYCfDKn8MpxApvqEVC7nsSDZC6NP32UEC1RmKRlzG4x+L+p+vWmU0RxPjTW19Mue6Et8yCl2JTwacCvTdur8hRSxIeTg2dqjq7KS2QS3RfWzQ4fm5bvtomqYz2ajrmQ5ihO7wUumrxe7AUg+xSKE1MhPADKnkW9GnzWXn0jeWM9L+RYqlIKRXVTqVv4OdrWdrcN3GrEg7fA9t8Hw/dhzJz2mClxySTZlgU3vD0p169o1/DFI+O0HaHMbeBU2bwjYjCbRkA4d00n3+ujKCowFAkpn+/L7P3X+tetWuPLBpzp07tzHSxCAKfvLNv6z3VBypisv322XSMUAdixdzwMJMoS+OOWaUF3bEmQUBvkrao1U8eUl3MopgDHJdLJ/g12435TJgGthFd8E3FKNnHmhvsRnZEhyLc1LBQwqVKv8Z2qNMi2KQBsvh5srfnBET1t2Nxkql/MztfQxuL7VCFwoJTErcGaQs5uM4ITyBmmpNq633mOLNYz7fOQ1qC/x9WtgNwndczF2Vzk4w+Vmfvmsrix75IKThDdvmdWeVQPRTvN7Pr1lVL0nn26bavs4So9UcZIswBj59Zd1LtPhlOBEpaAn8r3uIX8ByPxaZNP3KtNCN8O4QcMPjvBKMpkuO1tQw3tj3DAf4bNGeoxpJs0qDWtT+86mFuOaohSfmpv5a6rsSP49QW4ahWxfBIA6+FNt15sb5vNleLEgH8B5Q/YD4RvrDTUtarGh1c8rbVHXm7+TLd2M+3zg3dxNu617PO6RlHYT3pCtBnQ3rl7mS7ELACBaUZKVz5Vm3cIl3UCB2z6XvSWq6zNSXmZrO8FZ5FcwWWVMGGijy2lurOfLt5yOqpSFG5vdxE8a7eLvoI+tCbUqDQB4roKUPXUUsSA/KQoeovsZrm81ghM7vQ6ZJBZUBxoq40irhGrobJupTkf1X13fapRo2S0HDFfzAV6kiv9dkaoLS7EWVqG3O0Zeg/oSH3rDYdK40DV9TG4jFjQCewqFNHZeV94VwTpbqvfvr+tsX2IuUJSrBda5wzBWyC2FIDBg9znhxgWOYiW/AYiqJCTQxr/EAm32TNLyDWfud9MohEglv12mZVpvmG80F1Or0gurjACPCEL0JT4EAGcHIdettqeWlukgzw4LqxAWVtETOtVYS7U6e9Ib1e9M+HlxiEiV/WO0oMbYb+p6u2kUwpA9tEoZ4Q2iezysPGO0vJxXONIitOmmYB/VQtCZWvLPql6dnNA9gvyzd3O3HithpIG7R+X3+cAJhABAeDXzTLCAaoLHjRXCxFZQo8qe5HLxQAKQwie/Acj1regbtA9G/m5J/4da4wqcOIC5kJqb+TIiJ7MoiBhl7m3VKm34oszvS3MzP1G/aRTbPKwlEo4067oAzuzyutUIoRp6JX/mUuP4Z7Lp3y52JspWJ0jza42MGhUMzG0wxqgMSXMzphUIz24yvFoouFV0+CLoNYkvK0EAeiKrGoNFQfdmTp0Kl7V9qkPnrsd4CeatRMK7uVvnJiexQAB6htC143h2sNq/Kttk6V3H1coP/8YbupSxFC+qIFPVX5Hp/hDVNZiu+ILgDPLuWT/E64WjzKLYyVgMAm/dzDej59u/wQZ9J1SWWKwhV9+eDp5zfN28tztGjsWXlSDG4+x2jiBczSssxYL8Kn+oxhmSvHZd6JnSuX4k/PZWdLnAxSqNK5AN8FKY4CU0jX05xX0+iJcyLyctYr1fRlDzk71831rZET474g3lrSTOGw6D7+xZcsx39ixwGcZ8T3a8nbwKVjrYWWHWPR7HNCILqeOmDJYq2BEBhcIg7wHI8anbED5oJcfa67Zj+NQUekOVa7pgCXPFmAomYsvL2JeMaswRmCU/VG/BnEYj80ONNTdnvt8mV7RUY1LHe5MFNXwWJNtKXJzIQQqcV2WjyhkXa0ZJF1Wi2VJt9PqIl1D6BA+pgwzzHuxLHsUeouXlcO6OoSk7sxdiNmDI8v1slkP3eGSCV6C4KXi0G/ir7mXBWeQ9AAEAI5jgVm9PNNCrlrObkKszdjNkEy0Yk35uLJuoMh3OzoJcBHiughlTBK9cb0yhoUX4LIgqOEn408sicH0sqaJS0ALgM1mBtqyVtBQVwUNGMJ8qeVQ3jJfYu/T0kp4M70lqZGvBhwsydI+HdVEvxGBMcCaqa43LZALkP2MpWMcZAUgsyE8YukepXdMLTF4zurKCfdmoxuwSW16GGI+TgY/54lNlSBwbhHxe5kH2AyysQravJhY0SrHyjd3r2qpXC7dCbWJHrjcVqKbzVAm00apds5sQBuwHeGUVnQFTqe8J1lAJPajKGBlH+5xgx/zwqQ49n5n2bPmPnNhciyc217pmNVzoj51shpUSLDdlSSjEB8Q9OCMA+bw5m5sssivZqqZuwTKqwMbx5VYKVM3ynmiUvt5aqhES3vwrfaXju5KKYSTlTWM2qlOZjpZqYyxdU8pMNPX6O/gV9eAh9Wq7W0vtnAKXfVIFlr/6Md83kgu83fTf/zmksyVH/o68Zsyy5T8SvOcT9jwlNH5aIKVZ7sDtQQQAQElXl63t5Bp1D84IQEA9WdQSCVa+UTUmWEf3eNjyL1WGhBtzAmbDfKpgPO4Mpa/eHhi92RXR2QxJKg3mqoZ8lSRvpkhXFnd2k/GSpcQo6rcbvT6/frz/mGnI6ATFMzei8stJ+PkJe9Xr6sxJNlFda2VHAAYcoMeqXk+eLSwwVM8+WV12DnYbypNtN6LlFUc8F1XXmlyHhYFjAhCzIdoOcjGmj5npoIKJXJeFZZpU1bxMpTJHBLZUEGJmR6ixRUHrjfQt1YbBIDU5MzMdFHZVtPqyKIhpZ1JUgVK4mvfQyWMvgutRlST5O/jsVixor28k21S9bmT9KPLYNJ9NVKvEoWpebpv12RFyjh23ctV2JseevoYdV23rlMBFcA+OCUBMOH1/LshIJ3ARUkOV6XBqFsTNJWTKwDrhVRqUZaw3igpOTJngTBj7MaaX56FKxACMJvkf79dhdpMRRFHKVk0thp/Nz3fTWRAtIr0gdkj4DUllCkVwgoHPsrVHydEiatUujs66rOxOvtEi/HmKlJWRf69e/LYui33OgQs0sll+pQoyVIGLHewYZQruwnEBiOoB51pFJpdgZjNUWZBUx5xCqo30dsu3Msl5zxhOblaF1YbbRUGEGD3hMD5HkQXJBC3VvEEgF3wAXLhinSwjo1qxFynv1EmWAWEY4rXRBJ4pBuy3p8DmUsW0adN2Kp/Fgc5O8u+erT06J2Ma9/lEBctB2M2AJCOXQUY2kGvUPTgqAFFlM9y8ku0m3F5ulWnyHtiqshwJb+olWtx3UEGO6R5OZTrM78hEFgSADjZUfiUAxkT4J3t1mFdn+HtQvSBzGo3fQI1JFiTzKCbs8wYNyuGO9EGV5eDG/v113a0ZkLfeuln5rOZWkBOaxo/5/dC+dUrRvQPcRiE0oAvFgaMCEJNUMx0SnGSeVMutnJwFsSsn7IjrKpkaFhOgYDzunt6om0bxqls3jcLzgUZf5tV9kT0JtPG9IKpm+0iVKGKlChdkvLJK53ommv70HX3r6Afydz+pVLu4ICPQZqipFRi+jV366ZEjyTG94jirFhYVgzfXI8GJ4CQcGYDYJe+r1QVCsiwIt0JWqBmSvF9X/g66T2KxxmYo9CU+4zxZyYKYQQ6XBQFQ94JkAlWNfrLtnt2jw6y5RhBDycCaallUL8isuSgN6dYZv2+ZPjZIT1DR38lu935Pfs382GvL32FvzMXEAgHeLdobYu/ruAQgrsdKeZYbyqx0TXpACgHHBSCqVedkK9KuWfF1CanK8ibbLp9YMVzktsvunlmgpZo3TgQArocjY1kQVY+FN5SeM7rJghqEsiN0kGA6sFOlWHMbvmhCT/jVk0lu5T7QBvDSOsdds07EgwgjGCWkutJSgBmbyfslpufx8O5+mvcfGbCfz44k/EaAWmDo5SfBGw7TgwoTSZX7tCBkGgkyCh/HBSAmUoaVX9JtSM822QhynCxygPE4bY64KIjcmNnEbslUsdWPSXtKKDK5QmxKtFJZjPm1yJbDmD0gN9Yb21Pu6OZEcvfT/ceuW42BisN297qoODD5MXx54qPk9RBUKNN05nNxqOwI2+cxZehxgOtb6et7yJ5s7lXewLOD2ec0nh3MbueJRrO2T0JusFKCJXK6Qq5wbABiFyeuvruVdIKJbJ8HO/umCpzMDAmFU4LbvJsjUiVaN40ypG4zJfsbqeL7NfwdvHlg2RGA/9ilg7/DWNWm8IbYLEiFxwOBt26WZ0cacIHJ8N8t0SvzKJuJZcfYIOPPg+Xsdn8/uCJr+5RPOAUsAAC9hC+VE3Wh4sANJVgJDz91FYle9+DIAMRuSYzu8cjFlwUKUZaXw27ZWbZRmSPqS3yoJRLsGIDFLEgyaV8OLZI5Y78b642SKkoVy/T14Dw/YsEvysUo1avrW3HQoIPgIwKNE7EYhHsGZuY3FCMvrdO5MrZTsRgcvuLxvE1qpjO+FgAAccVdcSKmUKBzMedGnuMHFcaLbN+IIAiCDRwZgAAYqy2qSZOUYeWGQpPldUL5mOPhyrASXjrTsbAKkxoKpsKCGuPzqJ4PlZP2DfONXhHO/RwATnUNh2jX2H5/16fvMj53z52uC5qdwNDKIwDXraYzIHleOZ85YAA7dohpjh+/b5n+ocKsz9Vc9CI/xmQPB912UFc5pAvOQdVons3yqkx/tirLIRQGjj3DqlRwMvJdr19oaPEElKw/XTCyvMlw6u8xfXKoTIdq7LyhoRUWa7wBo7fbOaZ+VBbExMyEUP4e165BNkAJVyubcAWeLkWPR01JSQ73pD8BpC//vz20Si9hxoqSXz/O3lOn/vx9gIteyuHOCHYRqV3BLTg2AIFFQeRq3iXTkVt6Vg1EbjUiurICtTh9npyaUXB7FsSOshVXokWh+/TUJXlbqo0xlXt5KphO55T/h6rZfHYTnu/14AKiQBvAi4/237ZxBUKgDYb/bonrguacoJighk9OYzdry2Mp0+3H1ujctwc1DYYy7uxBTYODU7Y6/lmQUTrr+Oxh5V519lEoenLZO3L8yauVpfiCO3BuAPI5qkmTlGHlFm71P+HR2DEtnpAsSAYxMx3UtW8lQ5I2Kr8ObyizWZD5tch+nhmEUPTeR0r1SiHX6ys5DacKtPY/bWYuZY95YPBb5N99b92s7zubP/WkoKaxGRAAgB9euo4c5KSGXc+ze3Q2yxdo43u5FA73gpBrqhe/zb6DP902VeaALsHRAUjc57OtvCFlWJnFbtagZ9VARz4M7P4eN2RIOBKaZl2SF4Dv9wCg+z3m12JWJiqUNwiAEURw5oIAvHdJ4wojS/LKqv7nftpODJ+awqttFTOKsrcppaXk36PTduI/Ds1fc//LoRDccfE95D0bUqjKFWoDOo75AUD1bv4fmKadfeGU6YS8YMVQsFgZtuR3cmxcgqMDEGU9uuA43GZOmAyn/h4zy5dqFiSlXhAAPpuRrA+EKo2yy431hkEhVf4zuwkh0GbI7/alqcXInnBBiGosUsWPFStcEAgAgbdu1t+opQ0IAQC+P/ahvAXtuztp13YAgNcUxnrHCrQBfYjXC1C/nT4fqvtaIewg5B5VmVGh+HioyqyEwsDZAQh8fqMRK7GOd68uQJxuTpgqxZgFAUjBmNAbojMdLdXG5J7q95jTiBmfvHNGcQBGoMGhRQxZUarf45r1hqQvVaJ1w3yUFd8+XPQiO0ENT9vp3PshVMMOHVe0RB2dus25vykNVGIBORWRELJGoSzaqsqsVAGYlGC5B8cHIBiPg0fxonCye7VwIZIFyRyx5WWoezy2FLEsN+l5Q0ZdODfGlVsF2uiJfTpwqlYAxuosVTJ1fStC9yhe2SpUw09QufKuYiVSBTC3wXUv9orKP/GDXCaggAmfnsAPqsonJSB3FIUSZKiw20w+dNk+eW67BMcHIKqyETEezD35zIJkY8Lv5qwOxuP2X0SU10dfFtQgBA/Rk/v5tUb5E1VuNWsu4pBX7O0Xx/Wthnwu1YfQ3MwbFN4wnx9rbjayNc/u4XtMBIPOunzvgS26rtqR9/s0a9gJkLtH8WOqMisJQByFat6jGnNT4KLKZKjG2rdOKdx7vsBwfABiklVlHyEl0pl8q/xEsvm9duEkhk3ynQUBSL0X5LzjuZUgJOHnJx+RKnbVVE94AV5bntljM6eRb3JX+XeEatQTKKpkzGzGpRzVixHu+D3dpisNKFV+LUJ6cA3jKlRBNZft/NEhHdqaUv4qQcgWqkZzyYC4B9cEICqoSZb0gWQfu1mDdIKQbGB6mVD7Zap4OTULknXM7ALlx2GW5FBZkPrtqFxttQuXsZhXx/uGmJ4ilLP6jfW81K8AAEaTOeeQreSVVbpSLlnIPap78pNm8s+V72pQ2n46Szsk5BI3VYzYDSQkA+IeCiIA4eBq5IX0KcTJN2e2mAy3ZkEwHrfWkK7Kgqh6LCJVmc8gmGZo1Mr6jfVGsPSDI6l/JxXUmCVa0gtiGNFRJPxfmFD2wVcik1bHwQWSPzqkc/d43OeDc3cMLbjnveBsJJAofFwRgKhKraQMK7+kmgUxXdWd1pBuJdPhVHNCVbYvvqwEExp9m1tuSJ9XZ0zsn+qgJ/1cVqKpBVUqRLaZ24BsuQiHaWpIZUgEJUNtSre6Z621SPjVj3kTQgA2kOy6f6S8Y12Eqs/DTT0gKkTpqjBwRQACoDZR48YkOMkuyaRqPdH8OSDbwe1ZHVW2T5npoAwHKTiX5KrXAYKH6LFY0F5GIhkJP11SddMo4xxSEsEmfbeb22AEUVRWxayzL+IsyDHVfaxQTgr35M+AUCDoqAc4U0uPKUqzSh9oL9prv9BwUwmWUPi4JgDRy0+CXnGcHktyU0kZVnbhejriPp9jswYqCi0LojQg1GIACW/yL1hYZWQQqEl/U4vhGUIZAt40KvO+IABGYMBlZbggZF6dsS92DRaLFL3zCn7w4l/Qf//RIR0+a8zODqXLi4/q8Nv7HPnsySrdo77oh+pLq599RrhtIanYKRQVLKHwcU0AAi3VrAIOxuPsTZfQNOBKUIT0sWvY59Rsgxv3uTeqYJsca/UjaDF6It8Xb0jtHs6toib8tKFhJkg1MJhfawRLfbMdqmbp2U1G8FWMili/vY8t2/nygRU6XLeaPm5lR+ypNGUI31s36xVvL6bPV+MKhGvXkPtmqSfKpWDU3ukI3XOx4597QnJGfv3lgr22BXfirpk5sxKnL/EZkygGSTtmH271n1OXMsfclAVRjTkhC5LMgJDdMNBmbSI/r87w4qBKtm6Yj9BZB/DPof5jC6uQLd9KB7NmnQpuFtTwpVhmyRVVisUxa252MjlOx3STJ2iP8c9c+Oy67OyPRaLHv2LLA0R5n7gcLaGWFBcEQcgl7gpAAHjvggH7STlQfYkPwd+Z7b0qalQN3GbTuWo7p2HXgNDpGRK2j+qmUUY5k5VekFgZX7IVqeIzJLEgHZykS0u1ERhQGZwFNXzQYMr29uXfX9eV3hVFlgW5uioEXJajwsmZ5VlzHX0v5gPpiSxuxLhZcBoOfoMQeLuN/yjKjvANkdksAREskWpGIROfq9om3UxFtn5PuljJgpBBSKvfmh+GysSw1Y+Q8NJjLdUIsbLkn58NVE3wfbMgCT8fRBUhbYr6f9XY+eskX7z4qK3nQjZ2RRCcAMbj0gMiOAp3BSCLgsiVA4A3xJeRBNqUai1C+kRXVhiSrsQqerazBtzEgQs0oisrMNn32m06d3oWJCkWsiBpvcisuK+nysIqQ5aXylyYDemcQWHCf2GZFpcZATB6GiQ4Oc/Rqduce603rkh531x/7wqCAsmACE7DXQEIAC/3ecN8PjgZsN+QChWySmx5GcJFL7Lj2cgaqIKJZIFGsu/U4vZrpp2eBSEb0hcFjWOVJAhRZlJUBoeLNQRvd3ZKsebXIsSCvDQvZ8Bm9or0JlkQUkQ4OsgQBOECRrS8UjBZPLtO6IJ7cF8AEq6mTc8AwFdxGGDPnbSjcarGZYI9En6yTt7MkFCO3dleeeSCgWTf62ZzQlW9t7LRViHHmQrsSpsWyZ6krUpiFwDgl8/S54OTF+ZQ9YgUEv+xSx+/bxn9W3c/zS8mvHVzcRwfQXAZx56+xjULCnZFEyRwcQ/uC0CG7GGzGdFTU4B1eg3V0D4FQmZJQ+2ICk6soprspxPguLnpPL6sBDlZ3oSmsZK9GMWk3jn6Eh9qiQSfZdFidCZlYZVxzKzI/qbKghpDbYsQo4Ab5iMXHJ8vxbJqmKiS7C0kYkE4OGUr/VubWvj7YtrO4jg+guAidM1d5Vcqt3MJMgoD9wUgjSvQN/A9csg3aB8EmDEIHkprcixYpLmZ9UyILS9D3eMhJ63mZN5uEBJdWYF2SrysZincKMurQpUFsSpF6khVnbkNfLazuRnZBYpY0PhP+IIb5jvv/AqCwKJrHtcFGhzDlvyOfXe2b50iz6YCwH0BCABwt1d02k4MR0vpwUAbq2cvZJhZc5GbzCU0DeI+X453iMdKFsPNsrzxZSWoavxO2biQgFXVAsh9FgSALQMEACNDSpVbtVQbgXO29kkQBCEDXLLw/+pckIGJOGCiMJSuRLGr8HFlABI+N9Rw6KU4Uwvwqx/3H5uxGVmnZiHzhKtJEzh9iY9tUo4tL+MbpC2iyqBkUzqX257re8kpgc/IcxFfVoKcN0h8WQlaeQGYWRBlEEKRTTWpWXONYILqFTOb1SlZ7pZqY8yKH4ogCILDKKQMiApVdkRwD64MQOBEA0BHPT2WrIyCC1yEzDK/1nDNJtCX+JBrUo77fBC3WdmjKuM6LxPMkEyeMFkTPTdmBlV5paWaLUvSl/hQVXKVdvDE+YKYilvZyjjMmsuXW5nSvIIgCAXGx0/9WUE83zAeh+HfeEPmawWMOwMQlR6/N8SXWkkPSE7xnPMpJ7DUqrm+xIcYRdsT32Ra51x2Rfd4ksrDphNMpJPVyQiddXSWAowgQxU8JcsOKaV9PzekI7/bLMXKFpxDugl1vlv9RiO71YZ0QRAEh5DwuHNKR+GJRuH4k1cXRDAl0Lj3auV8Pa5vRV/FYXosVGP8J+SE+LIS9EQYt2SFU7JH5bBs4Tu5sdjyMoyXn+W34zxmeqF7PGz5mGosXn6WLIPKGYuCyB1XVWBlNeiyK5kIsWB2fEEADFUsrqSqpVrdo9I9gg3YBMEVUKXIguux2+fhtuBE1SuqKg+2/S4Sco67rsi+MOVUQU2DQe98k5bilCxIzilZf5rPOhATwNjyMoz7fLZds5XqTGVHWB8ZCFfTvQF9Plv30f9E+b3eEHAlabkiWTChyjoly0ipzA/NYJMca/UjJPzZm+ybGQ2KWBlglD8k3HkWBFcgPY+Ci1FVMnjDYXbMd5ZeZBSch2sDEAx+ABXBT1LfrupNMcrKIT2rBiK7WrNYQw9TmXTeSyLTzKvjy/daqvm+gd74O5SBCjnRvmkUJitJyyex5WXIBShW1bxUjetaIgEJjX7cYBST9uCkRSxIB7OLNdbLBBZrRiN7trIzgpBtsin0IOQNVaO5FpfV/2h5eb53QbCIawMQffouvMRPP2BP9ZTAqa7h9HZnJkK0a2w2d03og6552D4CLRJhMySQ8GZnwq4ykmv1K2VrAcAIVBJ+cnJqqnxRWFWWyjaqng7ueMeWl6HVc0F9vipDcr4J3mbGKymtfgRvN6v2BcD0qJiN8kXG197/rgRdhYBk+wuShEfLWqN59eK3HXPvqxalVI7uTnjHCtZwbQACAPBpNAr4ZmtqkruRKjEcyzE9qwaiFk9AYF3/huDoygp1hoTrIckinmg0edN4wg/YU8IOc0GVJxolj0MuUWU00u0HMf8NFazoS3zoPUenzjEez+6LY1EQbdUGlx0B+PfXHfNSzgU/HfdwUQZejobyrknGnEY5j0VGun0ebTuucv01Uwi/oVhwdQByqms46N0j6MHuUXStf1MLKw8rZA8tGmHHYoEA2wMQK+XHrGAng2IpU7EoiN5wmAwmYsvLMOHRyExAeHUVqjJCOSNb2QYASPj9EBtABxpxP12Gpi/xoTcc5rNhmYJQxTrf10OVW82vNV5mSXqDBICmP31HjpEg5JF0S1lV/hq5zo58um2qrUDCSVkcQY2rAxDQIsZ/FIE24LwPoHsUwM9elIs0h5y7YyjGfT5y4m2umqtKZOxiVzrXE0meqYiurEA7ru6OUCNZrCH8ZK+9e+CXzyZvSC87Qqp+xZaXYSwQICf7ZmYmW0FIfFkJ+k7R1yC0+o2eDyrQuLFexCuS4Nnao//6V/flezcKF7n+BAuk43UFYGSi7UzgnTTplwyIe3DATCgNGtby2Ywb5qNSclfkeB2Flkiw8rucvK1VVNkGLsiwKnOoxRPk51sxRcx3QzqenJSyTwsAGCZ/yVCVOnq789ogy76ItQg/0Ss7ArYDtiIgvqyEFTEQBCE3aBG+0sAq0kMh5Ap3ByAAxkTmP3bRE4NIFb+iyZVuCVnDnJRzTcoYj9PBwmINPdGo7Ql7dGUFqoIQrlSKy9j0pmfVQHbSlUw9yk72JJOoHOnNcXYwWUnSTaMQzw7mm7u1CGn217NqIFu+lglUgeF5FTTqt81pNMY4CWdBEIQ8kwk1Qe4zVIGJBC2CHdwfgJypBeiso8diQXalFeNxMRrLA1YlXXP1veHVVewkPJV9VU2YqYb22PIy1Et6aCO8HBKvOMUbJO5I6Oz+WZAr1pf4UEsk6Mn+oiByGZKsSvJCL58ZzoQwFqT3+aZR0j8m5AcpwRIskK9AINvPbKEwcX8AMrcBwd9BjyX8ooXuUFRlS1yfCJshsYgdoz0rpVKqkqrY8jKMlyDZ/AyLgsj2KeUKc9Wf2r/FGkKrnw7CWv1IbtMHjMchVuYlx7xnAuy5znaJmv9sN/i6euj9Cmn8izxSlffSOaEIkQBEEIQCw/0BCIDhME2t4i4KIsTKyE2U5SVCVlFlFrKZIYktL0MuGwFAByGp1LWzq0BcgAzgDJnXhVXIijmoiFQmzSLGlpcZQRbTkJ7w+/kMkceTXA7ZJj2rBmLC7yfLrc6fc0opbGEVxsq89mRRC5SS9af1rKuXFTuykCZYIJuZCNVn57ucWHAnhRGAJPP1YCRHtUQi+7KfAkuqfRmqDEm2SbbqHVtehglNo8t6WqqNCT7VxDy3wRjLd29BS3Xqgd9izShfSybpe9Mo3pcnCZx7eiY4r9ZFqHpFV1ag7+xZesNWv7FdIUL05VhB1QslZADVAkG+FzCEnHL8yauzeq/ZUZHyhmnZdUFQURgByIIatjYb43H25oj7fNBTJbWL+SBfWRAAWt1JteJudTVeX+LjMwnBQ7wsdMLvjBILygfDhCu3WqwheLuTf3bCSwZn8WUlRj8G589R0pNdD44B+9nz4olG+WA3eAjg148X1sTvn0M619ujWqiJ+2X1M+uong/Vu3O3H4JrsZKlUPWQqDIgCb9k6ITUKYwABAAgVkZOVPQlPuRq0GGxxvePCDkh11mQ+LIStuk8Xc8RADDKrVSZDsp/5sZ643vznQVZFES2tCh4iF9pZcocL8C816jPX6wZGQXq2CwKZvcebWpBOFNLPjvCq6sw4ffTZWYzlxoy3y8+WjBBiOeczzjegvNQZUA+aebHknj2CIUFl70Y/o039PatU5Le21o8wY9lQOJXEHpTOAHIgAPqWntOzSfh51WAhKyiCibMxm5uu7RrXZnrIaFpSkPEpMppN43iH/LNzazyE8xucobCkjdEBwk31htlVFSJzmINLZXuBNqALV3yd/AZolgwu/fonEb0dA1i5aHZ7ZxyznJAz6X78r0LAsdFL/JjssBWcNgx/bNatqXKZqreuSLDK9ihcAKQ+bX8ZECL8A9ieUA7Gq7sKa0G5cWakY0gyn5U3hiWMySqCbO/g890JPz5N7uLVPESu6pVWFXgZTKvDiHhB/j5btpnIxakj81NoxAq92a15EmLRCBaXk6O6SW0WhYAGIamxbCAwVwT+ejHKkpUfY4qJT0LctmCu8jmZN+uk7rI8Ap2KJwABMCYIFH14qoyjvm1/Kq0kHWiKyuUSkhcE3J8WQk7ZglvSD2hZhqrdY8nuXfHghpD+ekXv+j/72Y3GZNwqtzoplH5LwlcUGPsH3Uf3TTKuFcsyO+yzGk0Spcompt5WWJ/B2Qz2xBdWYElZ7roCfWiIMKuiE5eE59nhrKl1pUrStaf1rmJDW6L6nBmIrutrH7mgHw/FwTHkO0mdEHIFYUVgHhDfLNe2RF6QmiOUauyQs7gGuRUE35LwQBHS7Ux0aa2X6whaDE6CLHaN1R2hF999HfwsppaJP+qNp+b8bFjmYCrTdci9L14fasRuGQxQ9SzaiBf2tfqR3airRpzEZ5IlPy7Nxw2rnsC3ePJm7loMYFVb7JjEwcoFIjy7TMk5AxVadawJb+T+Y3gOAorADFXnim0CJ/GTvjBrkyokD7xZSXGhJ8zw/N38L0H6awMJvx8FiThBdBi9JgWUStGAQDcMJ9vLJ/dxDekz25C8Ib4YDlXeEN8aVG6QciCGoQB+wF+9eP+n3/DfL7XZF6dUYqVxcZvldmlShUL4/Hk/UEOJuHRbEnpxoZ8ko3dEfowQqFgNEhV/iKZk4KDCyZU8rmZWCBRVRxkUy5dKFwK76qJVNETp+ZmZFed59VJGVa+8XcYSksUqnIpf4f9GnxT8YcKJszghvpss98hWSnS3AY+II4F+QyJN5T/2m07viCpcO0ahI56eiwWVJ9vO6aJFomurEAtTvsDxZaXoRZPsKIJnmjUtS7ptmu4ZeEmJ4xQyJzOYHqXAEAyIEWEKsuRiR4NLcErZFlR2BKEvhReAKIKJEI1ak8Bu+U8Qvq0VCNUvU6vbi+oMcZeWtd/bH6tEVjaNFBTyo76O/gVRKuT4ISfLqm6sd7IdFAlRc3NhqBCvpubF9Rk96USruZ7Tbwh+rg1rjBkc7MoL9qzaiByajCFarjHTVC8m7t1Vprzn0O6I/xrigAv2rzsxEG9aPh029S0n02SyRBySeFdbapG3pZqZFeWw9VZXVkVLDBzKa9kVnZE3TeRTqkBJ14wv5YPEhZWGdkNzjfDpLnZeClQ5VZzG/isXLYn/05gUdAILKkytc/LrSreXkwHaDnIEKkU2KhMR2x5GQKA67Igygb0eBwSHvo14Q1pmesJEpS0x5hyUADYNGotew58vnNZ2R/BeWS7z0OCEyHTFOYVdaaWz2ZoEXq1vNXPTwaF3FF2BOC39/U/P9etNpqQqVVxU7nJbhZkYRUftM5pxLRXef0dvP+FN6Q2IEwW4LidhJ8tvQsMfgu6OibT23XWJe/DSQOVAltseRmawQY1BuCuICTh0dgGdABgm8xFejN3nFQEIGN/v5S91gJ2MyeCIxm25Hc6l+nIRAZEEHJJYQYgCb9aDYsZ8/TorpfTdD3XrUa2rlxl5KfqG7BCoI1XQqt6HeA/dtEZkgH7kzdFz24yAhwqsLqxHiF4iC89cwqqcrB0AoHZTUYvCOVEPm0nwokGOju1sMpo5M9imZq+xGfr+HPBiZPhSssSit6DQlD+cguTS0vZscNhvja/K1aYr/hixW6QkYngRNXnMXTZPpk3CSlTmE+nVj+yK87za/kJruAMukfxUrQJP50VUPUNWMHMdFBmd40r+JKfWXOtlQOpsijeEMCJBn483+aEAMZx55ruvSF1b1UyVF48pnkhhRbJa7N+yfrTOtWsDuDOIIRCFWRIAJI7Xurq4gfP1NJ//+19et7FLISiQJrQBTsUZgACYKyIcyU5jP+D6XQtWZA8M6dR3RvBTebn1fGqU1YItPFZlLIjAK+soq+LQBud3ejNtWsQOuvoXpCZS/nfa/ahZLHp2hIqp3PTU4XDirhDws+aPwIAnWVpqTaOTZ7K1HpWDcRCbUoHUJeReTd361KClTv00KX8oCpAl75GQRAcSuEGIAtq+JVpLcKuSCc0jTXFE3IMt/Kv8sngXMatMHOpsRK/+2k6C5Lw0xkSVdlYb5JJQXNw8sS5JuHny61iQT6ASHjVwQWAkbUEoL00zEZn6jNuGsU7twtJ4XxNkhH3+Zyd5cm3glym4d5lqv6xcDXANeude44Ex2E3kyElWIIdCjcAAQCo3Euvji4KIkQqyU30JT4Eb3d290tIzrw6ZF3B5zTyTeOzm9LLglzfymdYQjX8RCBSZS1LMbch9Qd8U4szsiCLgvxxb/Uja9zIuGhT/073MT8xVgbsfRkLit+BTRJ+v7LPgwsyVJ4AjqDQglKuRDF4iM+AcKVZQtEhTuiCEynsAORMrTFppNBi/GpuoM1+L4GQWbhgQIvQTtoAxkuZK5eyQtkRGPTON/tv39RiBDdUFqS5GVkFr75QDe0mXEN7U0t6ru+Zoup1PvvU6ke23GqxhpacwhNeumTLDGKoSaVZAlZIE84cYpaeFhTeEG+mqcoaOBXu3veGAAbs58cEIQXsBirSAyLYobADEJVGvapR/aIXAS7+RZZ2SrDMjfXGOaIm5de3GhPy15b3H7t2DQ4qVTRtJuOa9TiMK8NrXIFsXXXwEGDZseSff30r+t66mX7QN65A0nARwJiE7Lkzv5OnWXONcjOuxEWLsD0fllSlzECDWhxYFDQCQCoTNL/WGQEaA9es7mS4JnPX98i5sS+Cy4CoyqycUrop5B0RjBCcSGEHIADGQ5hb8arcS69GX99quC1zE0Ehd6gMCP0d7Iu5Mx4HfLPV9vkrRYTx+5bxk2zq2qjfjmP8fpj47reTfm+lxwND936L/HcVAz6kx65bbUw0qKArl8yr43teFgURo3ycYWnyqkWMkiuKQBtfWhI8ZL//JwVUv4Ebi/t9tvst8oEr9tVun4cbVRC5ZyAXmADkVSFOcBYqn5/eiJeIkEsKPwCZX4tsGVYsyMuflh1x50pZoXHtGmQn/NeuMSbCREmUPn0X6twk1gLvTNqCh3p66MHrVrN9IoeveBx7LNTHt9dtRy9jEtZ11Q5sDykmD511ST8/6yikd/WSHl6yFywEIYuCRj8J9Rlmjw9VBmY6pGdZtliLRFiFKIzHWZd0p6lGWSqJY3BE2ZZKeW1+Lb1/T3Xoykm7E+FKTQHUZVbh6izsjJBPxn/1efZaqF78NjuW8Fib6kmviJBLCj8AATACDaqko7kZoep1ehurykZC9mlcgRj8gB7TIrzLeMKfVrbAi8hmKaBhLTsB+zASSW5OCADHz/nUPSNUuZX5vao+klywsArZIGRREDnFLMsT11Y/siUkcxr5e3NeXfrO9UmIrqxAlVKeqtzBSQ7pusfDKv7pHg/rgO4YVNLQHKreEKeiktNVLZK5LdASktJ1ySW2tjv+5NWWrnkp1RJySXEEIAD8g7qzjvcRCFfTDcdCzhni9dKN4TOXInhDEKB6KszaaJsN6dFpO7Ha5+NLuV6ngxN9+i6ExhXJH/gzNvOyvIoAB7whZ2TntAjfAJvwK1eoLZVixYJ8Y7m/g2+G93fQUsoZxBONspkOALqEKba8DD2RqGP6QTzRqHWFsl64epLCLVY4mYTfEKFIlYtezMLOCPlEde+pMqxWMxttO66yFZxL5kSwQ3EEIOaKLMW8Ot6FeXaTu1bKCpj2uu04ubSUHKvwJqBUYy7lWDAtNZiD4TCUMKVSUL8d2QD1F7/Q4ee7rRnwcdkMb4gOnmYuNcqQ8t2QbjZ+U30XrX7k/D8sG37OrzVKqihDUVOKmSvFAshqEGIGGlQQYmYOqLGeVQPRajlEtrHjd+Tb2KU7PgBRKRjakcF2Mqo+j88ac7cfQk6wW8aZ7XtWekcEOzjjTZgLvCHeGT1Sxa+0HpvLy/UKOWVvdzeZBem6aofRT8GULFV4E8CqTiUhPG0nhqftRHYy21lHixw0N6OlGmxTVYv6/BmbEf2ddBDSsFbtPp4rZs3lA/jFGusNklIpVuVeeixczU/AkmRgMgU3IVBNFHSPx9FN3p6tPe52Ob+xvrAmQ6rrWFVmxWUnBdcSLS9nxxLcIhxYD1zsZDJUvSeCoKJ4ApCWal6mc1EQlaUkTih3EaDrqh04hjFNa6/bzk7IuyIBiJ6ekN6XcxPduQ28LO+8OrSkyhSuZhtG9c4r+O8OV6fnd5IpIlXKpnPWGwQsNkIn/PSq9uwmoxeEWiC4Yb4hPpHFhvTY8jJMaJqyFEvVkJ7PUizP1h42k5HQNOVkxvG9IcWEKjhxY7mZoESVyVB5cUiGQnAixROAABi14UzJjG/0/0evki+s4oMTIeeEEgm63wMAWF+QGZuNbEQ6jdsJPy/nnPDzfURWhAxmzTVKjajPmLnU8EKhftf1rWn1uGSMBTVGEEZlEVv9xj4SQYjlLMjsJvR+Mplu4L5pFEKsjA5CbqzPupCEvsSHGI+zPR/chIFzGM8VcZ9P6WZuybNFyD+qxTFxQi84VAHI0GX7yPfA0GX7dG7M6uerMiOuzpYKeaW4ApA5jThy6B/JoWj7lyD68d/Q21XulWZ0h3BwylYMn5pCD87YjKxE7Q3zMS1d/NlNvLrS3AZ+bEENbyzYmxvr+VKmhJ+X1IxUOWOi0VKt2H8vpO0NomLAAd50LeFXZ2eyjCpb0LNqIOYzC+IIKV0hPbis/o/366z8vOBaYoEAO5ZgqgN0jyftICGhaWyAIwh2Ka4ABADCug4jf7ek/43U1GKUaFEuyzOXWqvnF3KDNwTDqXMIAODv4KVzAdJvTOYay1WqTKaBoBWokqEZmxHamuiJ9HWrjaCL62HKJQk/XVK1WEMtkSADDTODkCwIUZU0nff64SSBI5VkM3ymUDWdJyPu9+VFmtdOU6rre0PcChdk/PY+nR0rOwJs75RQdJzYzPji9EElTMGVeEl5l2CXogtAAogQ4koPYkG+ca/sCCu7KuSY61ZjTNfpsqTGFdh+hgkWZ81Nr3HbVEWjgphkimlWMmg3jTLKraggZ14d38OUA+8LS5jlVgTxZSXI9RVYLUdSSdzCoiB/fBTN8JkiurICrboN9yZfpVi6j7kcn27T2TGQcouc88tndS5zi2XH1Jk/s0RTKAq456vu8cDgle9ZmrvYEdWQzIhgl6ILQI5O3YanuobTE0LVBLVxhTNUhwQAAGgP+/nm7EgVP+HXImpn4WTMbuJLjZqbkcygARirmFa+N6H4XQP20xk6AIARz6pNDXOEqndA93iUmQgrDenKlftwNfv5GMW0nL+t0LNqINotJ0u7DC0VVNmgSBXbV5DQNCnbyjWBNuM/DsYvCEf9JFt7JOSROFNmVbn6A/3UhvHsvXly44S079shy+k+R1mUEOxSdAEIABgvWS6YiFTxzcqhGr4ER8gtMzYbgQDlOD5zqREsUkGIHUOvvqiCGE7o4No1qJxImMxuQlb1qbnZ6GOhyrSuW42Or/n+3PSOCgTiy0pQ93iSBgnKcqeFVUamg2h415f4UPd4st4P4j0XTlndKra8DOM+X+6CEC3GBhkYRSObRG2maFoXssOgwX8wnmcEeqSS3Y5TCxSKD5WiXW8Gr3xPt1qqJQiZoDgDkJlL+VXs61t55ZyZS7OuqiOkgMptXIuoJSq5TIIVbpjPZ8Oub+XLoRpXIBkw9cUb4ksBVb1IN8zHtLI7uWCxhumumCllYFv96OHm8YHPsu4N0rNqIF8mpmKxxipmZZyElw0y9NIudjMtInLkuSagmjwqPEC8nHmq4GrKP/uM/Hsiie+QVbhSLd3jYftIJAMi2KU4AxAAY4KqmgxyEzmV5KqQHyiVKTMLQgUaN8w3HqTpTNYTfnXTOVcOZcVTZm4DQvAQLTc8v9boE+ECKPO3OZnPMyHU33WfbrlhnGve1iIROgBoqUbsKcl6pqFn1UDbQVbeDQqZVfWclogJ5wmrsk6KZ8lRCRYLjksW/l9eClfTYMAdH6V9j3KlWlazKIKQCsV7VTW1YGDge/SYaXDGYaWURsgNjSv4bJaq1E6LqF2Ek9HcrO4J4q6f61ajpYb0hrXIroc3N/Pmh25H0cjeG7N5m5oYmxkSajJv9qhke6KvezzspN23sUtXGRRmXRWLa8jfkVCaE6okQIXsUKkIZDFAr4YDAIwpKcnG7gh5JFYagHBlJTt+5oHRWVt8Ui2oSHAi2KWor5zw6Qn8KjZnDLegBmHIHnX2RMgt4Wr6PDY3Gw9kLgsSC6bf08OZEwYP0caWAJbLgKLnhtJKXwDG/nNZlj136nk3J0wHpo+jL3azDLkoJVI1a0dXViCnfhVbXsaOZQLcFtUh4aUHvd2sOpbu8Yg5YR44HCpjx2aUl7NjBzoHZGN3hDwSCwTYRQDVs9BqaacqkMhUiZcg9KaoAxBoXME37s6vZWU9feUfw9AqRv5QyD2z5vISrGYQQpGudK0pvUsFIQ1rMRpnHszXt6IlV/aGtQgzNvP7f+0aeqxhrbt7lSxmQc5L+xIlW6pmdVWGJJM4UTFK9+n8hESLsMfeE01dYlhIH/R3kn+veHuxfqinh95oz52sdK/gXvxnzsCAjz8hx8rb29ntvOGwpc8v6eqC6sVvk8/EspMnLX2GIKRCcQcgAMZq9I8O0RORsiNArSRHp+00fCgEZ8H1RcSCfIZE1cthhYSfL+WKVNH9KQCGWtaeO619L2eeuOdOnc2C+DsA32x170Xa6kdLWRBzVf6fQ2Sgwa0YKhvZCxwuk4HnKnK9K4KCK/9wi86VYIUSCTgRo0vphgY71AIdgivxRKOACXrxQJXlsLp4gPE4tO24KuVGc6sBjiD0RQKQBTX8g3rAfrZcZoTfTzcJC/mhqYXvi5jdxI/NaUyvn+LGeuP6oaRxG9YaGbafvUjL5qoUrXrjDdElf+bnU1y7BvUYX77hCixmQkCLKcvauF6MnPRbOAyMpj4v9W7uZntDhOzxztkonOqhezmCmgb69F3kyVQ2rguu5dS4cXDs6WtSDhBU7ua9iSpK+lRIdlSwiwQgAMbk5QdHyAleoLSdXEneN2kLhs9ekpPdE1KAyxZ4Q7y/y4D91rMRFFrEyJZRdI/ipXPP1NJ+H32ZuZQPNG6Yj2y/x4zN1hre3U6rHyHhpdWzPpe35Zq+ARygPOUEdkV0Oy7IjiEdWW2HMsinA1eCqWpOD0t2viApVZRBhar5xSyr96/q3326bSq7ciGLE4JdJAABALhpFKtoFI57QO+8gt6uexRfYiPkHtOfgyqpamrh+0SuXZN+FuSiF2H8vmX9v9fMkFBZEDP7Qo31xRtSywZzQUioJr0SM7eg8NHIZlO3q9iRYIMMI4g+xm7q+GM4ay6/f1b6rRxI0Ka6UPTc0AzvieAEVEFG5eEP2TGr5oKBU52p7hIAWM+wCEJfJAAx0SJ0FqR+u1EqQ41lwlVbyCyxIK+P7w3xmQ4twk/irTBjM37IqSsFD6n3yUozvMr88Jr19srP3MKuiG7FG8QTjSpLqgo5C2KllMwTjbIeLNhTYizEELhCZrMAg+xyxYp0NTHpM1X3xg7o7Dd25R9uKbjjU2xkO9OQ8Ni7z12RIRUciQveLDlCi/D+Hica+Brzzjq+iV3IPWZjOVWS0bDWkN6lyrRmbOb9RCwSjZbSfUHNzQiRKrpPZHaTsb+cnG9vVKu8KrUs1XZuoNVvySXcDCa4QAPjcTLQiK6sMIwDn+pw7X1sJUOhOoaqSQQnzesoVEG8SxWhPk5RLjo6bSdyjevvTNri7meAoLxHP37qz9I+v59dwVR6gNoEUZrQBbtIAGKyoAYh0Ean61uq+dXn2U3o1hdcweIN8cpUsSA7WRk/qB2a/vSdtLIg4ZPT6LEb69XXSdCirPOvH9dZDxpVvwcV/LgIfYnPkiqW3VKh2IBwesaUDsCztUeZKbLlFbArwvuGOIkb5hdcAM71eUx899s61+fRGY9DZ4or5YPe+aarnw3FQrAtfwbIqgDHE5EmdMEeEoD0pnIvXyqT8JNSnwBgbPN0mzzEncIN842yI6pnYuZS40FKTNYPTtmK8XQbODvq1eUgVCmf6SdipYxk5lJkG95Vsr+BNsc06nq29pBO4MnwdjMO3gRcpoMbg4VViFFkFbPcQHxZCXIlVqrjjdui/G9OeHnndCFrfOmPt+qVXjrwOxuPQycjwRtKJODwFY+nFHCduvIJVwZoxYaqByQTKHs5FPObWCktdS4IyZAApDfXrkH4rJEuxVgU5GvpW6oxbVM7IbOYpVgUWoTNgrwbDqdXLz2vDiF4iHYwZ2rsASD1fqLXv0W6u/sqDtNjs5uMXqZ0nd8zQHxZCSrrhplAP7a8DK0ELqpSrOjKCkz4/aTTur7EZ6nUy40oS6ySuZxblUMWMkZM1yGA9GEPaBocnbpNJHiLjPDqquzeh4qFBk/XIHbs06mXZ2NvhCJAApC+dI/i+z24RnUAY0JrpY5fyC1Uv0fjCkMRi2g6Pz51G4YSCRj7+6X2z2XwELCyuZzks7mdFTngGZvZcq7otJ28v4i/wzFlRpyDOQCAN6Sl3EzeF6UufgnymU6Ln+9omFI1W87sjBO3kF0O9fRAG+OvoCqxGsJkTQQhKYpF1HjFKX47t4ucCHlDApC+LAryL+mWaoQB++mxeXWySug0Zs3ly5WuWY8TB9DNc6FEAg53jLD/vfXbkS2Haqk2MmmUcEHjCiMDZwWVolf3KLrcqrnZOB4OUAw6v+JOTJZVfRyx5WVoRfYxtrzMyLJQ2RRzRZ8IgMzvdnMpFpWxsJ3ZkclFXjiXSLBZDhXiASLYRlXFoTB6VY4JggIJQCi8IYAf76ef5JEq3jyuci9d/iLkDV/5xwC/vY88J++Fw+TY8anbEDrq08to3TAfoXtU6tv5O2jX877M2Izg76AzNapyrhvmI6v2lmu83cB6swCfidA9Hj6L1Iv4shIj4KLO46IgQuAzcjvd43GH9Gwm+OeQzpZePN2my+QiP4TjfAavXRETxiQAEewi97qQY4rkLZsiLdUIVa/TYzfWs2NDh+yHiWWSAncS0Wk7Ec7UkmP69F28w/icRr6HxCplR+gsxYIaIwtCKVPNmmvsrwVp56F+gMNnKulBb4h3hfd3OKMhfVHQ6J0iggmlmtVizQgsrPSzKAIcTljifI8KJzrhAHo3jnNN5ObflSVlsTJgVa60CMDCLNedCyTDVdVyTIklvtmqn2Ca0wUhKaoMiENKd4XCQgIQDkXDbkXVu2T/QHvdduxJJNgVdyFPqCRuY0H1RDYdF+XrVvPCBfMV7rSddbwnTS/a67Ybn09db00t/Nh1q43vVjmr54oFNeg5PYwcii0vQ9abI9Bm7aU4t8Ho96HKzhZWGQEQFQgu1owA1IIBYj7o3TTONZCbf9c9HrVZGKOcJeSP4+1MY++Lj+pc/5fuBrlkofCQMk3BJhKAcCgcpLuu2sE2AR/+ZDrYKr0Rssd1q/Er762kJ5Iqtawb6/mGbqtEqvgAhyv1a6lG6KyzJu2sckHn+l/M71aN5xCMx/lV+kglvcI/a65xD1o5RqYRJEWgjV9RjmLBSNDaakCXkoz8wQXX4WreWDFSZWR1BcEOqvude8c4OEssOB8JQFScqeVrzc/U0hPLpha+fEvIGy9M2Ihs786cRmSN+jgHc6tct9pYgacyKXMb+Cb5RUHrJWBahG54b1iLcKKB3v+mFt4VPsecbxingonPV+fJIGR+rXX560gVfS/Pq0OuFEtf4jPOncs9fpT9LN5u+u9PdUj/R7749eM6WzpYuZc3XZQyGSEdVM9SJgDxhjTwnXb141HIIxKAqFhQo57gcCtRx+Y6wm9B6EP3KH4yyZ3LuQ3p94IE2oBVT1OVgCX8lnpB4Jr1CIeW0mNzGvlm+HC1Y7J18UFH2bIzZQmRVVUvM1ihjufCKqMxnwpQCsDjR+O8IXYkdOwpUWwopRV5IRZkg4mhZcy1+Nv7dMeISwjuxEYAGwsmIDpQkm6CPSQASUbwEF0rb0qaUmNzGhEq9+Zg54SUMCVwKW4axfcbzK/lx6zQuMIIAqhsw9wG4+lNlWKZTdpWSNDmegBgvFioXgaz4d0JfQ4LavjjsFhD8HfSwcOcRmO7n72Y/DcoJnbKMU422QUoXc693Xxgl/Ab94uQH65vJY99+xmmJPTaNTg0IAGjkAaqAIRbhEv4pVRTsI0EIMlobkb2xlTdeLEgBN662ZWTloJm4jrwqc6LKtBINwjhyq3C1cCW7cWCvOxzb1r9hkcNNRFv9SN4u2k5WidNMk0vHSpLZTaMU6j6eHpjOtFTn99SbdznVBA3v9YIIJ0QqGUSLcI3oMukIn+oFh2IseG/W6IDAAwWE0IhHVQBSKyM/DNG0eiVEwQbSABihViQz4J01tE19jOXyl3pROq3Y3TfvfSYSnI0E3Kk/g7aJ2ZeHULlXsA3W2nJ3kCbNcWqsiN82UzlXv4FU/n79DxPMkm4mp/8+jv4crXuUdYCxJZqhEgVnRkgDPwuGOP6JZxGn0CJbUCPVPKf4fKyM1fDlWsCgK/kdL+/HZ+6Dcf+fqleivLKEdJAVcLHeSaV9IBe0pOlHRIKHQlArBA8BGxJlTfEeg2EP/p/HdHkKxBwvSDZDEJmbDYMDpkxPXQpPaZF1H4WJs3NRhaE8h6Z24AwZA8dyMyrS1/tK1MsrOI9UkxvFmrsplHGdhaCEIwi6D7mnzG+JABgnAc3NKT3ymqwvxOAnVTAD45IA3oeQUZ5bezvl+ojfD5yrETT4JyYEArpwN3zCkEKzzkfeEMyjRTsIVeOFa5vRfR30qvXN9YbEzuKOY2oWs0S8kRLNea19OjXj/NN51TwMGsuVlS9e77UQknjCmSDlZlLET5rpMcW1CDbQ5JrVI3fkSr+RRkLqlf1P+e8bwb1e80AiCsDc0sQYsKtaqp6Wvwd/IKLkHW8TCbjcLcXPozwfR4HJj/Wb8OKtxe751oV8ouNPg+Mx9WmsYKgQAIQi+ixMphSTq8+AQCvxBOuTs/MTigsmloQjsynJ7+NK9hMRNdVO/D4x39u7Tvqt6NyFZ8pt8IoqpuWc0ksSE+S59UZ/RiU/vyioPEitKJNryq3WlDD95Qk/O4pT3qqQ1f2r6lUrkxxBCGnDN37LT169hJy7EsDESYEAuTYp9Eo+feu8ICM7ZtQ4HBBRk8JcGp5cSYjJwhWkADEKjM2Y2csRq9CX7uGf1k3rkCsejObeya4DWWzn8KbI5lre28iVXSQc9MotgyMc9TOC/PqDPNALtBgmiJhscaPUaiUw6jvNpXUnKSKxf0GVYBhx3RMyDqDvV6YMvgkORbTdTLLAQAQ4qSWBSFNWKU8ACjp6srhngiFhgQgKdAZj8OpGOOM7A2xE8fJpaXQ9KfvOGfCIuQXc/WdmjiaGRJKcam5me8h6UusDCBBq+IkzXQ4xd02FmTVV8Dfqe73sFJO1urn+0bMMSqTNL/WOH9clinXcNkcb4jvW9IiX6iC9YXzxBGyznvhMOybtIU8L6ryqxjX/3HNeucsKgiupLy9Hco66LLeaHl5jvdGKCQkAEmBrqt2YA/3oL9hPlu3vm/SFlS9PAQXkok+AG6lmQkcAMDocbAiB7tYQ4zHyUDDzHSwY/6O9CSHM4U5saZ+rzmxpvZzsWbdwXxRkPeG8YbUddFOzxTYCSR+vF93TYlZAaKHL2LH2hWXW1Dldi8IydgVYRvNw5WVECmjF4KkBEtIB3lqpYgevohvItYihiMtwdl4HL58YEX+J3VCZmipxrSka80VdorFmiH5yk2utZilAEFf4kOVRCKrkBQLOscHYrGGUHaMHlN5g2gR670aXKbDzBBQ5Vam6pZTsiCp8HQb3xsC4JxzX4yEavgxxTkLSAAiZIm4zwfRlRVMJpWpCBEEC8hTK1Ua1uLQIYyyVeMKHFtJS1senboN3z13Los7JuScdEtVFlYh6fwNoJ5AK6SfSYiSKn2JDyF4iDY5NCf2Tulz8IZo6V1zTBU8cNv1/bdahD4WZs8HdZ7m1/JBZL75wZELMxm9j1Hfa6v3WML/RYmZkHtUgSHzvBn5uyW6KjsiCElJeNnMe6CzM7f7IhQNEoDYoP2zOoCX1pETm8PhBOuAfupcBYz9/VJnTOoE63BO5Al/+pP0siN8EODvoCe+C6vQsmzuoiCv6KSaQKukcHONOSHmggBviD6GC2qs/wZ/Bz/5U/mwJPzOkuV9qkOHfw4Z5RS9ezx6/z81ZmbUVBNgIW/gm606Bj8gx7yIhvKdINgk0NnJBhrhIZIRFbKDBCB2uL4VfRWH6THVi+Ca9XiCa2IX8ssvfqGzUsreEC1du6DGCATSYXaTMUmmJtfz6vjgASC1HgSub8TfAfDLZ/kJtFMc0lX+H6pejViQPrZ9ubEeldK/AAA/e5HOtATarGVacoHZG5OqqdjCKoQfHdIdm9EpBhT3oR6+CPTpu8hnzTFGglcQrBKuCkK4SrEAwyHPCyENJACxSfTsJWwvSLhrLMCLj5JjAU2zZign5JbmZj4bMKcRlb4IXOBiFZUL+YIaZPs9FgV5v4/eqJq5m5sRZs2lf9u8OudkQW4axcvy3liPEKqhx0w5XytByE2j+GBwbgMfDJoBilNYFOTPmzfEl/bJZCK/aBFg70XFQkS1VyFaIQhW0CIKURTJgAjZQQIQu1y7xjBEo7huNXKlDO2do+D4yXFZ3DEhLVTZABVW/Tko5teisu6ek1IF4KVU+2I2r3NwZpmzm5AtQcs1C6sMjw8qkFoURCg7Qm+Xiut9ws/39oSr+ZK7WNA5DelmSRi1PwpXYwCwfj0JmeeG+fyxV5yzIRKACJnAzgKEBCdCGkgAkg5lR9TZDMoX5LrV+NWRx7O5V4JdZs3lswEqmpsRZje5Y+LG9Y1c38rv/4317vhtXEM6AG8s2Jf5tcbiAdXXsaDGyKZwY1wvSq4xMxxUMOENGQEZVTImGRBX8g7jG8LCZOeFIkaLqBe6BCELSACSDjOX4vH2y+kxhULSa2fPArz+LXkJFBI/e1FPKwuSC1TSv25hscaXk91YbwQInGyuVWUvVQmT6iWtRZyxIugN8ZkMMzNLlY2JAaFzuW515iaHqSjoCY7nL99b6ez3jiAwSACSCahekFlzceTwN2DQO9/sN9Zet50t0RJcypxGdMWLfVHQ/kTGKQ3pZhBCsSiI7ETa35HaKj/XWK5F+GZ1LeKchnQK7vc/u+dC2V4rPTOCO5F3T0HxmwkbJXMhuBIJQNJl5lJ2wtMei8GpKP1smDKwB678wy3yki8kYkG67K5QqHo933twIZyyV6CNDhBMyV4rWRBVuZWpfEUFZGa5Wj4n8NwE80eHdAi00WN9n2HiBZJzcibRnslsilAYOCFzKxQdEoBkgkgV2YQcnraTzXTsm7QFxb3WpXA11M3NOLTaYZP0TOLvYJXfco7ZVE/1tMyvRQhX84pZVleATYNCioSfVy/L5wrz0220zC6Asc+ccEagrX/Q4YR+liLi8BWPS2AgCELRIDPgTKBaPThTC/Db+8gXeSiRkF4QJ/PacvrcNK5AbiLeEAzC37//XXef091P66QqVsNaIyvAKWY5DS544JrMuX9LySCb/SZU0/uCGsyISaVduN9tNqD35Wcv0v4fN9bTzepCblEFgnJ+BEFwKRKAZII5jYYEKLUy3tSCIwd+Sm62b9IWHFkii16OZcZm/uQw5VbP1azHZ/54dVZ3K2Nw/iVNLciqYpUd4X0kco3pb0JlOkwHeE6y16p54MIqIwtCBSxmQzoVoCyoyU/DvxYB1hyTO29zGnmPEy1CGzAKuUN1vw3Yn7v9EIoTKc8SsoQEIJlCi7ClF0dPjgN4ZRX5Ej8WjQK+2Sov+EJi1lxM25wwF8SCbHYOXlmlw547SRlpAHBOKVarn1esWhRUe59YDRC0CD8JTPiB7avQIrkvY0omhEAFXf/+us6Wjd1YbxxfN1zPhcqcRjqg/PfXdVEuEwTBrUgAkilmLjVWValAo3EFDi8/S26mT9+F08vKsr13QqZpajFWuH/1Y3pilolVo2yX8Mxp5BWjrlmPbI9D4wp+LB8kK6mi+kTm1VkvkzJ7QajG8pZq3j3dlLrNZUM6dz5/cESHcLWxT32DkFgQWPliAMPnprlZUrVO4tk9umNkn4WiBRmRHUGwggQgGcRX/jFwgcbxk+OAXFEGgAPhMPjeullWGB3K0L1Mn871rXyZzewmtk/EMgtqsl+DX3aEV+5K+Pl+j0Abnz3JNaZvB9WPYXqfUGVa8+oQukfx5oy9mV/L93yoGtvNDEIuUDWgq7I43pBxrS2oQWW5lWRBnIPK0d7pfkSC87AZyOo+udQE+0gAkkGi03ZiTGduyMYV7FJB11U7cHIgkK3dEtKkvW47v8zT1ILw0jr6pM9cmv7yEGUYl0muWW9MnqlJy/WtRm8TFTjPXMr3DeSDBTV8EKCafKeCN6QuceIyHVokNx4qXJO5uQ+mytW8OrxgX3sfN67cB8DIhEg/SHbgJKU5En4juKXOl9PksgXnk/CyQ54exaWp2E4QkiEBSIap9HjgywdWkHdsoLQdAkym45yuiy+Ik2F6eABAravPlWg5iVlz+VX6hJ8v67luNR985YOyI3TPhapZ3MyQUGN9MdWtOG8QALqMaW6DsV22+0FUPS19z6+/44ugs28Py89365LtyC0Yj9MDPzqks6VxXFAbqsnUbglFgicaZcf8Z86Qf8dtUZ29bgXBAhKAZJiDU7bihz095Fh42k4Mxz3k2IHJj4kviJO5Zj1yQYhSROCG+a4okvUNf4EeuHYNQuMKdIXB4rw6IwtCTdhaqvlyuVTc4efX8pkf1VgsyHtwZApVlqdvEKkKimY38b9jTiNKiU9mwW1R9fGkVM1U11O2rzOh4Kj4+GN2LNDZmbsdEYoKmfFmgaM9Oozft4x8qfg8cbh6P+0vcSIWg4nvflte7k6FabzWp+9yRZChIjptJ7K+JwC8ud51q9FRXjZmrwaFFuEbra1mQUy4pneuIX5+rREAUT0kmeBHh3RWGODZPToZnCT8RiaEO7dcFmR2kztU3lyCrTp6lcwzp8omCAxdl1zCjukeetG06oMPoOqDD7K1S0IRIAFINqjfjpNLS8mh6LSd6EV6vlrp8YCPGRMcgKKPpyBQ+Z64RVoYwJhUUyv7qjKplmpj1Z9qVu/Lwipe+WphFSq/XxUEpYM3xE88I1V0L5GZBaGY3aT2McmHx0mhEmgDfYmPvve4YPqpDl5wYNbcwn5OCRlHi/D3c9znI/+ekIoNIU3kCsoSI3w+NtPxYSQCw3+3pN/YG7XGBJDbTnAxuWhCzgSvLdfZfpeEn25Wr1c06ecDVUO62ceRie/gGtJVpVAq9aJ0CFcbTckUqmBB5SOhCjrD1eLCnSmCh9ghPFfR/487ErqyN0sQUoTrAfFt7NK5DIju8bDBiSBYQQKQLLF19AOsItbxqdswnEiQY0FZVXA+jJwyAAC8+Chf4uMGBaEZm/kSpmST51z6XVhBtT9UKdSiYGru5Qk//R031hvHkJqgqzxF0kGlzqWaqJrnlAuQtQjAL5+lG+sBcm+0WIgo3Mz1iuP9/6jFAGJlxn+CkEV0jwfClZXk2NmhQyFaXp7bHRIKCpntZpExJSVw6xF6shpKJEjvj1cv34SVHg/bQyI4gIa1/Ip/qIbOEsxpNFblnTZJp+iso4Ml07iQW/nOld+FFcxeEE6xigukTMUsK9xYj2xguaDGKNOiyq3MsUzC/Z6f7NXZoMoMHkxTRioImTXXuG6pQCPbEtHFwmeN9N9/9qIOZUf6/93fAeDtNgIRQcgAXCYD43FWoS0WCMDZu4bJM0CwjQQgWeS5mvW4J5T6RONQTw+MKynJwh4JmWLs75fyrtHcZJArkXEas5v4EqZ5dXzp0Y316BhzwmRwcroARgBpVenJG+JVh8LVfGBg1YXdCj86xPcDqOjtDaLySolU8Y3qveV8BXtw5y7QRvf1BNqMsq1WvzueJ4Lj4TIZ0fJydkxMCIV0kQAky+w7dw4odaHotJ3oAVrC9fAVj+Nb3d052T/BHoeveBzZ8pNIlTrT4YYsiBbhJ5aKmnVHZUFM6V1qor+wCqFyL73qP7sJIVRj7TyZpUhUVshUKqLG5tXxY6miRfg+D9PlnBszMRvSqZ4PM9NDHY/ZTZj0ehd4nurgM1QD9tMByJA9fNmWFREFQeiLv5P/OzcmCGkiAUiWCU/bieilg4lw3AM64yTaHgGyREtwAebkkmJ+bebLb7LB3AaE2U30xPX6Vn7l9Zr1zlqVNcutqGzH7CZksxeplEmZ/5YK2FSfY2Yf0iV4yFKQUbL+9IX71/candPIe4PMqzN+B1Vu5pbMnhMZsocfO9EAMHMp7QHC9fVQJVuCqwne80l25wE7Ejqb4VRlRkXuWUgTCUBygB4ro83q6rdjwBMnFbGgfjtWMuoTgkPwhnivBFV9vIskTAe9803y9w3d+y09wAXIL63jx/KBqjwp4ee9OcqO0A3YFP4O/ryqskkA6TdyqxrQe00eelYN/OKa/I9d9Mp7pIqfcPg7+EkH15AvqGlr4gPRzjp6m4560u3cs7VHhzMTyU08W3vk3LiU0D0XZzXA94bD4O1m+okiVV/IlwtChpEAJBfM2IwljL/HIK8X6BwIQEzXYSQVnAjOYE4jQnNz6g9ns2zHBQQ0DYbu7W802F63HcNxD5AmhNetxgonBc9mKRY1QW6pNs4FFYSYfgq/+nHye7C5GVnFqDmNxhgVrJqZhWz0Ufzqx7pylZIau7EeoeyIEaD0xewNoo6HKUss/SCZI3wR/fczEwFCl/b7M8bj4A2HyU1ELlXg4GR2AQA8qrhVZKCFNJEAJEeUahpUvL243918fOo2HMM0nJ+68gkMiCyvkEeOT92G7WE6g4AKFZ7OOJPZyxfprOJxDuN9mTWXb9BvbuZLsfwd9ktnXnyUdjkHADhTS5fwABgr6NxYU4tRmkZlZpqbjd9IBSFzG4zfaCVgEwwUASLpzbAjoQMAqUwU9/n4yaTU8bsW38aurN5P8fKzEBvABK7lZ9ntsEeEcoT0kNltjjh15RMYZnxBQvE4/OPh29mHzJcPrJAXeiGy+2l3nNdYkPQ+0afvMlbECeWr6LSdeHzqNmdlerSI2iGd6hOZNZfvE6GIBensgfn9v36c/o5IlfVyr75wgYSKZGWA4Wp+hbN7FK+KpWqIF/rhOT2MHSOzFt5uAG836CU9/Yb0kh6ILytJ3YxScCy+jV16dGUFeU69m7sz8/7g1NYAlKIievnJjHy9ULxIAJJDqr1eUr71nUlb8FBP/xcKAMDBKVuxjXEpFVyOarLqJBrW8tLC16xHuHaNswINDtPvgirFMg0CqVKsBTVoWa3qhvlGUPbSuv7/vqkFYcB+Wlxi1lyEAftJxTwlqkZ5VVYlmWzvvDrjdzzVQWdBuLEb5iO0NYkaU7r8c0gHSryk7IghOkAFFKogww3CF0I/uOAjoyRT0eOQa0pIEwlAckhbLAaHw7QD+t7ubpj47rfJl3ZnPA5/e2iVvNALDbPHwA24JchIhh2/DIDUnOzNIISgwpsAtuLa3wEVfroUgkU1CeAyGE+36XCmNvlnt1QbJVVUZmhOo3E9UEGI2XNDjQkXkLBTYjtgP22YKQGI8DmBdSnce7EgL5qiuqZU4heCYAEJQHJIdNpO9HnipCJW11U78EuM4U973XY8l6ADF8HlqCRtnYbdEiEnYSoOcVkQzr38xnqE4CHrGSt/h9Gf0Yeuq3Zgj67TmY4Zm7Gre4j10rxf/EJng4wfHNHZ0rGyI9YnD8l6Z2SF1D5PdehsGUvCD7Ao2P/YV+7lP4+bLD67h+8TEhyNqsyKcygHANC1FERAVIsyFhX2BMEOEoDkmGi0FDhFrHAiAX/53krygXMyFoOmP33H/RNAwb24KWOjQuW/Mb8W2Xro61uRlUbty7VrjHIvIpjQp+8yjA6pYKZhLVpueg/VAFy3mv4tCT8/sejtgG4VKguysMoI2KjytIVVhpoWZfQoGFTuZX1AMMqcns46YANL7nzHgnzPjuBoYsvLbD1zE54MTe1UKnpyTQlpIgFIrpmxGYczkojPjHsYO2O0stAbtZvxfaZPRBCEFCk7wqs1DdhPZi8AwKi/pxrJKSJV/Kp0ws+vIEaqrDmkJ5sAmBLDfUm1IdksqaIa+M1AhpLfndsgLukqwtVsUM+qWYVqaH+Qn+zV2dVqb0ia0F1K3v1bVH1kkgER0kQCkDwQSiRYidLJpaVK1SvxBRGEDGC6vFNBiKos7ob5xmScajKn/m3ZEbrp/PpWY3JOZUFurOd9S6zCTRzsmh7eNIrPDAUPyWTEDgP282OUbO6uiHHuqExHpIrPgHhD4lbvUlhVM1D7d6jGUkLl9SFBrZAmEoDkgfa67XiKyXT88NJ1GGJqOw9O2YpepnyrkCFd5AUhXT4PEEgaVyCbBUmFhrUY7RpLBzpNLXy51Y31vPJYMn72Iu1yDmBMKFKRFe4Llekw/U8oo8X5tXz2pNhR1ddTvR4J7xf/9aXqdUOtzepnCQVNSgGIKshQjUkJlpAmEoDkkUHvfJN8KQ/z+eBLf7yVHJs1cGDRKWLp03cVX9Ql5IRAxWFe+laLALxC3GuNK7BiwIfWvyRUw5sZhqv5TEfCT8sCA6iDDC2izkhwE9VkNDcbWRsqmDAzOlTPx7w6I3siBoUXwimR/fvrOhl8arEv/uuLKqi02rckuApVf0hK6mp2FzrsbicInyMBSJ4IT9uJQ7zEShYA7L7sEQwzqldbRz/AZkgEQUiN8LSdRhaCKoW6bjUOZ5yAu67aYX0SbxoNcupaCT9vkOgN0QFKwq9eQTdLzPqSbtmEWR7GfTYX+HhDUqbVFy4wOFNLj5l+Da3+/udWFYCkk/ESXIm+xGf9+aR6Jpxo4MdU2RFBsIAEIHmk0uNRen9wGZIYALudIAgpEguyPRMZc3OfV8dP3BfU8GMA9ARBVdef7dpsfwctyTyn0fgdVKbjhvlGmdaeO+W5BaD2SAm00RK8qcgn98aqqppQOOxIWL/P7C4MSA+IkCYSgOSRN2o343th2njs8BWPs/0ex6NRGJSpJjMXMeidb+oVby+WCUyW+foHq4vrGM9citCwlg80MqXipEV4WdqEnx6bX2v0qfTN0NhRp3nxUV0pq/k574cfU//e5mZkJ8LNzcb+/vY+MqME3pD0dAEYDejBQ/wYRdkRXhaVOx9iBlmcLNYys3AiWUshi0gAkme8iGymI5xIwNC93+o3dmDyY3gsEmGVtAqVU1c+garSl/H7lhXV8cgWP7x0nfTc9GZ+LSqbqK1I5gJ8kQWhHNXN0iYqs9DcbJRi9WqKHz98H/s1wy/5P/TAZ42WyibGBb6d/PwrlMIGDfwIBlUcJ8fQ2836IBUdnPt0qIb+994QHYD86BDdMwJgBDPiWC1kmp/s5XvQBMEiEoDkmei0nWymo9rng2rGMySs60B3kBQ2gbdu1r/CmDVWKrJCXFN/MaMM2Iq1VIbLUKhkTOfVqQOU3sxp5D+n7Ai/4th79fu39+kH28eS/+zq/ct1tmxMi/CT3gxy6son8NSVT9D+FtN34Ui/NK8qM1jU2M936xCupsup/B1qvxm7ogOCwCE9XUIGkADEAXQx/R4Hp2zFSo8HrvzDLf3G2uu2Y00gkJsddBDhaTvxhQkbyRfqG7Wb2Rftq5dvkpdwHwKaBlOIawsAire+1+7v5jIbFP4O2sywqcXIglBjMzYbzfIvPsqvdgPAGx8omkZVk94ccnDKVrkX7Ujjmk3o1N9TdbYXBEHIMxKAOIDwtJ0YZGTzYroOXIYknEiQwUkxwJk1Tnz32zq3sl+sx+rWI3Q2Y9+kLchm0a5Zj6QEbaEzpxFJrwsTboybHFKY7tdUoHF9K2902LjCkLP1dxj/TzB0xIv898qKpXPgrpWf76YDzMq9RjkVVYKlkkOV8ishGakKYAAYzxK5toQ0kQDEIcQAyJ6OVy/fhGNKSshtXr18E4YYud5Cp50xcjww+THkVljfmbQFv3rwtqKbVG8atRa5oOydSVvUK6ecR0Yho0VoUz0AQ96WCg7mNBp9GqmUrtl5uV+zHicOoIUrAABmVlSQf//qwdv0iSPetrxrQpbhlKn8HXSgGKky/jOD196oygPFq0FIBvccStbnUaxZciFjSADiEI5P3YYBJtNxXTAIWz69n5zYDPF64S+ZnohCZijjoZKM58dvcF2pQtOfvpP2+eV8ZZRcsx7R253uV7sPU+WJC744B+BZc9FylmHmUiPTQQUs167BscFuoBTfAm/drA/yeCDw1s39t3tpnf5cZyf5dc8fvBIOTH7Mddd+IVLx9mJ90MCP6MHgIeP6yxQWVM+EIocLUlVZ3ViQDoYFIQUkAHEQEwIBcrK5ZNhd+HNmYvHq5Zswphdd/AEvT3wUuTKsQiMTkstHp25DuyVo5GS30GlqQdbALRakjQsB4MoKnXZPp7huNY4feBr+8fDt/f794SseJzOfXw4G4dXLN+GU0tJ+Y387osMwVqRwSP9H2lDZJ5cR9HiAM6Hl1MPOl95RcFm3n72oiwmhYBtV9izLmbVlH93h+vtcSI4EIA5i92WPILeyX+31sivhbdEojCwySV4AIwjhxtYdv1f/20OF0cPwzLiHkW0WT4Gk5VYE+vRd6CtW2VR/B+1Z0dRi9GIQXhfvTNqCcM16ywdsRnk57AnRWZPOWKzfPR/4vFcsputw9f4LMzQ1TKkmABROvXYBlH2EEwm2EX8Yo3rIusz/8lmdzcjFglKCVYyk6v2iUlDjrq0sc4DxRxMKCwlAHMaHEfph8My4h/HjaJQcOzhlKwaYJvZiZfXwu103a1aVWuVTclnlvVLQNKxFPVZGj127BpWTO4tZkB9eug4743GgepOOTt3Wz6jULKV7Z9IW/LtBgy7YjpWhfr2/l5BruXaN669FVd/e+z09/f/42nJDgpfycPGGeN8Qf4e6P0RwNk+32btvF1ZZP+c/OsR/R8LPB7BZDmyDiqy/KiMvJqfuQmatDuPliY/i197/LnkTRYlVT5OApsHY3y+Vm68Xz9XwK9HLPrpD/3vmOOeL3Zc9gv/S/oC9ZvEsM/HdbzvqWOWMhJ929QYACLSBj3sZpqA41V63HbksyOErHsfegWnvsqzVw+9GMyCZ+O639d1nztBfwE1ehbwQjfYvnzs/Fu8/8RoaiMCgwX+AiiGEiIA3JLX4hYoq2/eDI7l5HnMeMlkOQFRl5YMU/Z+cYqjgTCQAcSBt0ShpnHdg8mPIrQzsm7SFHRP6s3X0A3iCUdLKJz/v7CRXwwEMM8V8GSoWbQPzNeuNZnGqIX3GZgwg0kFI/faUjhdn3AcAcDIWg6v3L9cnvvtt/eU+gcrRaBSu/MMteoWmwSFq9RwAfOUfA1y3OqfnT5XN+/oHq4szmDVhJpa+t27WfZ54v7+3h/1w6lwFnYlUlciI7LK7uWkUf8+qxgqAPw/y1zVrtAoA08uYjLXgSCQAcSAqxaKYrrOu3pf4fMBlT4T+jCkpIRuA80lU19nzH9Q0aBowICPfY7eBn5KKLnga1mLAd44c6orxj9CmP31H5zKWFFfvX65T1+MbtZux2ueDq8vKYGSfHoEDkx9DLyJ8GImwL+YZ5eVWdyFjfBqNss+iX54+TRqvFgOD3vmmjhq98OEB4x7vRyzIBxoqQ0NRwBKSobq2VFmOLPdiqUqoVaqfY/zS8+QmJABxIG/UbkYvIqkE8fLER5GboO6+7BHsdOCqvlN5csyD+P2xDzlqJWn3ZY8gAL2C/MKEjViSoV6fUDzOGjOqJs3FqLgGYJiFkoF//Xas9HjICfXuyx5BrqeL4o3azbi3m5Y9fn78BgxoGrwwYWO/6zWgaaz/B4BxrnPNO5O24E/HPUzeW6eufAJVGR/41Y8L9iIb4vWyk6RwtJTMhE2sPANTBp8kt5lSoThUkgERkuEN2Qsmst2cvvtp9sJWZTkmE+qAgnORAMShzCgvh2NM07kXEb7CrAKEFRmSQmbiu9/W7cjM3nrkTp1zCs8XL0zYiBVMOd1LXV1siVYqvKMo2XujdjNymY5Kjyc1xbXXv6UXipkhV7LXXmcEIdxYKlm2IV4ve29/2NND3tsvT3wUr6uogP99ekO/sa+8t1LnjExzgfJa4cweQzWGCVoBcrB9LBxnnuucOeHJWIy99li1IK5vSRB64w3x5Vwq5bxsq6spPn/DvsvZMbYPTnAkEoA4lA0j78c6Jpo3SzIoXp74aFG6ox+Y/BjaKanaNGot5qtx7dYjd+pcrfxPxz1M+pzsvuwRVNXHpsIIn48NZgZ6PGTj+cEpW7EtFrNeQlO/HVUN0KrP+caH33PUJKqurIwN7oMeD1vW9v2xD6FVgYgXJmzEcQH6Ov6nIUNgQVX/Y3n7sTX6nlAI/mrgbf0u5FQyMNngaDs/WWAnGTfWY8GWD8WCvFcLc5+0h/1w/Fz/5z2+2apzDe2+8o8hUHHY/n4KrgW3Ra0/N1VZsny6oDPBeDJebrsowzsiZBMJQBzMq2fPshNELyJbCxlAdNzkLRecSyTgmI0JV1ciDrcdvSvnx2vTqLX9ZFatsHr43aiqg7XKM+MeZoPVs/E4nGRWXSs9ntRKsRTSqZ3xOCsT++QnMUcZz/103MMY03WgPFn2JVEpU3p09GHH6Aexb0P5lw+s0O/65BPY/Nln/UrndrS3ww8vXYfmv+s95kFUqsGlw1tnNyU/NzOXImfaCLObkM10zG5yVGlkpvANfosf5CZ1jIpZCSIMDdDbVHo8fKAjFDT6Ep/1867qIVIFGcFD1nfIDqoMi2pxogB8gooJCUAczO7LHmFX578/9iEkGxbByJCoGtkLld2XPWJL2WrH6AfzdrwOX/E4W+708sRHkevHCCcSGSnFevfcOTILcXTqNryuooLsB2mv245dkYB1x28A1q1Zn74L2XricLXjXihv1G7GakYG8uWJjyKn8PTChI247vi9lo/XpNIAjN+37Py/n1FeDgenbMWDU7ZiDOCCIOhcr2u3vc/1X5rF7N608lutffj1rQg/e5GXK/731x0TZGabEZzR4CurdBiwv//fX3zUMBokApDwtJ3YXkerrXXloe9HcAC/+IUOP9+dmQyIqgSr7EgKO2UDVQDSuIJ97oyt/CwbeyNkCQlAHE5nPA6co/esgQPZSeiHkQhbS17I/Fmw3FY248NIxLYyVLqoTAY5adVXL9/ENiynQnvddozpOmnu9NNxDyNbYz5jMx84UGgRNmBBfydUvL24/1jjCoRAG9ssny9eO3uW9UXh+rYAUjPH3DH6Qaz2+c57+/y8s/P82L5JW7D3NdNbF//A5MfQPM6D3vmm7hip6ViQDjTmNqDTgsxswpZ7ekP0pCvQZkwEZy5NKZIcKWpAxUlzM6aUPVQFGarghDO/zDOHu/Np2SukigQgDuc3EzYip/pw80V3sg+ack0DTxGa8uwY/SBuGHk/+8P/9tAqnQrMnh+/IW8H6+jUbUhOwMFQDOLcXdtisYy4XHsR2QlLKJHgvyMWtF4idc16BG+IDEL06bvwO8OG0dvVb3dcT1PXVTvwa4MGkUplv5mwEcfvW6ZzAUoq8rPHIpHzzcd1fZ4BQ7xeGLr3W3rgrZv1fpnQz80TT50eDUe7Bln9uuyicuSe24Dw68f548KVcLmQAKdil/AbQX0f0N9pS0734FnxhBIsoAr+VcGJaiwT2Gxy50oSBWciAYgL+OXp0+zqPFeGtfuyR3BQERsTcs3dqt6FmK5fUPaSazhXbS8imaGITttpZCGY8iarnLryCTwaoc32zpdIvfgonaFIZfVa4cj99MmT7O8/GolkJNDKJPdfci+OZ/o6Dk7ZihXMfVnp8YDVhvTDVzyOXTEN8M1W/aftF2bCXpiwEdu7gxDuGdi/0bxhLZL/n2+0CMCze+jfHqniAw0mcHUbvrdu1rleobFldKaq2ueDCq+zAnAhR/zyWfqa//lunVWQ4/7O4GP8jQAAsPL3/IbZLsFSwR0XAGjvHJXLPRHSRAIQF/Dq5Zvw5YmPki+uZ8Y9jJRfiDmWiT4BNxLXdbJ/4fnxGzCUSJCGeq9evilvq+1dV+3AaJwOGAOqTFaG5BCVDavXKJqYA21KzfYLuHYNQtkR0ufh8BWP4xi/n2zwDpuBlsOCkEqvl1VdG+z1kkZ8h694HPtmM5TUb8chXi/t9/C5S3uUOneKxv+8MbcBYW4DvV83zEe4vpUea1yBymvQJZDn6XM4ad5TsRhEU/TeGfTON/XhpXwpoOASuGe7XeUqgmjXWHZMjymeUwplw4ygyrCovvu61a5/ThQTEoC4hAc+uYd9C1UyTbEAAJmSbHUbL0zYyDakH4tE2MxRKB4nsw05IeEnV3q7rtqB4biH1vY3V7gzoBaF3m4yCwIA/EO/fjumtBpWv53NmhycshX3ffRn/LYnGqx/Tw64/5J7cfeZMzB0b//AaPdlj7Ay2qmqUrWH/dCm6C1hz30syGaV8sqvfqyzTekqHKSIlmm4BYAKjweGc43rDE4rWRRs0txMPydmzUXlWCqoghm7SlSZYE4j/ztUY4KrkADEJdxx8T1IrQ4DGBMhruH8bCIBtx9bU7AvbhUxXSczHUenbsOYrpOlMF1X7cA48OVQWWXGZmQf+vW02g0AGC+KDKTE9em7EM7U0oNNLfz3X7sGUyqR8YbI8wIAhrwj9VnXrDcCHYdNQsOJBKs4dMfF9yAVnAAYKlZWDR19vnPQHqqif3ssSK4Wjv39Uh3KjgDnE5FXukfxk5ufvcgHJwW8usldCydiMdLLhXsXmByfuq1gj1XRkIOyQ19pOzuG/k52bNnYrizsjVBsSADiMhZ/RPt7cK7W919ybz9PgWLhaJKXMNcPkpLHRTbgXIwDbXR25to1mLM6caYManwFX0vcj5lL8XiPTr9gr12D7IvvutWOU0w6deUTGI57gBMK6GIyavsmbcGApllqSo+eG2pkuvr89qF7v6VDw1qEa9Zj3wns4XDCCNoSfj6rlS/mNuDQmh/T17K5umknQ+JwOGECAICJgUC/v5nXlD59F903YrGXSHApdsoOifJWFary3nmDaAGL24+t0VVVF4JgFQlAXMS+SVvwf3SeJseeq1nPekZMKytjA5digJroHL7icQzrOpnpOP/Cz8fErWEt+so/psfqt2MFE2h2XbUjMyueXB3+5/g89Gr/wSlbU/p+nycOfzmY/qzrKipY6WkItLGT/bxRv5316wlP24k9TEB7NBKBU9Ekh+2ldfoFcse9gtP20BdlcUf7BnRmhsGhQUh73XYMn55ADyYrsbDac+Qw4oqFjRcmbOz3mys9HrZUNJxIwOErHiePU2URi48UDD/fnZqfh8kN81N6DnedGcOO7T5zhv776dOw+zQ9DxGEVJAAxGWoVvXDTMnR6uF34x/Ope64XQgcn7qNnQC2121HrkQl2jMwYw3eqaJqVuWMx3KFat9S/ZwJgRK49Uh/Ba/fTNiIb549Syu/1W/HmpISsu8in8R0nQ2M9ISXzByFp+1ECFerVcy8oS9Kj65dY0gZU1mo+u14QQ9R72v382b1XAYhG9v6Z/H6+fM0rkA2K6BFCk4ViwvSufKrSo+HXVhQSaxLAFIA5CrTq+jl4BZH3pm0BWdXVmZph4RiQgIQF8K9tNuiUQgx9ejtsRhfd1/g6LEy9eSLGjMnbQ5TXsopzG//y/dW6n+ZAZPLHaMfxNfOniXHjk7dhty13BaNOs7pmSuTAYAvggOKZGpVfSci16zH81movg2k3hAESoyVyX5lbAm/ITKQI1ZW9/9dncQ5OzD5Mfr3z25C8HfQsqIzl+Lwcvq6cSqcLDiAIbVLMaO8nP08lcP9CDEhdD8qQ0GuRBcgZfPhL1fSU0B8s1Xnynor3l6s33HxPdJjJKSNBCAu5ONIhCwdaq/73LSNmDgemPwYO6EreGZs5nsHVGN57jdQNSnnpEmeaXz/zYSNWK2oAd752VrL+zYxEGBfmseiUfJ3nlcFc1hDuj59F2saSZnMnUfl1cGp1Lz+Lb2fhv+MzTimpASG/26JPmvgwH5jQU3La/nak2MepH8nV25y3Wr8yoT/JDc5PnUbusmgUKVkxfXvTS8vJzNJAACbR44kt9n26f36hADtTyO4CJWfh2LRgirlUzGGCVa9iBBmKgdqGP8jQUgVCUBcSNdVOzDaM5Ac02NlbOlQKJFwpixnDkAtxv52nycOpBN5/XZELZY3Wd6wrrNNypkqhVKhulZOxGJsgPTuOesN6d8f+xCeiMXgSkLVp71uO7LlJKqsQh4pQcxYeRgXLESn7UTUaInpA5MfwxOxGLxA1G93XbUDSxhTS6fywoSN6jItl2QoVVmJm4cMIf++snoNUpkkAIC/GPAd8u9Lht2FFZqUYLkexeIX1+sJAPClP96a0v1AiR8AGGV83CKTZNiETCEBiFvxhugJ4ozNOL48Thqk6dN34bgiXb3Qp+9C7rUcnbYTObOvoKZBKdMImm3a67Yj94JIiiJNbxVVkPPChI3I9SNtHf1ASsFRKJGANsazJZxI0MEhAAQGvue45urwtJ1otzysb0BXgsg2/XsRgbueA4jgU5ToOC4PapaaME23B05cTJ/nmUtRKU/tIMIKb45/66BN17568DbdjpHshpH3u+KYCPZQqVq+051amSWXfQtqGgSY9x5XMigIqSIBiEuZUu5jJyB1ZWXsKvQYv59cbS4GVP4eQ71ecvWo66odeOrKJ/L2Qn/18k3sd/dr6u1FoPxj16wOH5yyFYd4vWSg0XXVDgzrOrlqH562E3PZ12CV8LSdaDfLMH7fsvPbjfT72SAwqGngQyT7uio9HghoGpmhCk/biUFNy2kTv2rF9jyzm/ia9+tWOzLblQqUlwcAwN+//12dm9BFdZ1VwRIKHEUGhOqlMkllceHrH6zW/yejZlVXVsaKGah6kwRBKBZeWaVzk0xV+Qy3olwMBN66Wecmh6z0q4P5+ger6X1+/Vu628rtek++e4NvtrLXOQA4ThHrPDYCwN4ld6pGf1NWe+K739a/9v53z/+7r7y3UjcXGK78wy06lQkFSG5k5xh2Rdyxn0nggjBLwZlQfPz6cf66yJAMddOfvqNzPUaqxa3/PPOIXLNCRpDlFRfjKzkNnFLFjPJy9uVW7fMVrYmVqjTluRobxk95ptyj0U3c9Yr+CYfSFo2SmQ59+i6EhJ8NnMOJhKv6GgD4/o5TVz6Bf/95QDGZKb97LfSofmVpGQAAfKWi4oLynrqyMnhn0hY0/59bQZ9eVgZ2yntyTqvfdfdkX/7yvZX6G7W0CEGXojRLEEi47Mh/7NJTEWb4w7lzsPb4cXJMVcaXSo+fIKiQAMTFRKftxKaBA0mJx5cnPspOQA9O2Yp3Dh+e9f1zIl1X7cBwDhq4M83Gtvv0b3zY30xyx+gHkSvhaK/bzjfwOpCuq3bgIE5da8Zm5FRZvIgw1IHOvIN8vIiASrb3mXEP49C939J/3tlJji87cgRuvuhOBDD6bZoGDICfnHxIBzCCDpPvj30Io6DDtk/v77cP3x/7ELZFoyn9HsEev1EoEznxuhUcgEqBkfPu0CIpKTcePTkOTp0aT46pqiT2hBhlPkFIEQlAXE44kWCbpGPAq2Js+PTTos2CuJGV1Wvwpa4ucuxQTw9bPuZDdF2ZB6v8FfeQmYNTVz6BMeD9cfLFqSufQC+iUk6ZY7DXqzQd7c2SYXfhzhMn4Or9y/Ud7e0XjO0Y/SAuGXYX+Tlv1G7G/3V6g6OOWbExs6Ii37sgOBE/LUww9vdL9bGVn9HbeEO8bHeKdMX4qSHXzyQIqSIBiMt5fvwG/J+nT5MqMb+ZsBE5VYwDkx8zPEME13AiFiMn569evgk/ZJRR9k3agm4q8zg+dRt6Eelgon47ehHJ/qbjU7dhhQMbdtvrtuN0m02bXODYO8thEk4kIKBpZOPolk/v19cdv5f8rHs++SRl6U4hcxxjslBjf79U53qihCKAyWQcvuJx5Hw4/vtUNAQbrOINATSuoP99uJrd7EA4bPkr7HLrkTvl2i8CnPfGFlImfG4o+8AIxz2sVOlgRvlJcC5epoelMx5nm7ErNM115/kkI8vrAWDVWbyIjmyurikpgduPrSH3666P79a3ECVSPYkEKZH59Q9W65Sh6KuXb8KZFRVkcHLLsLvwNKOc8+rlm3BqWWnyH5EHVI2whQJnzhjTdaV0r1DY+EpodSp4ZZX+wkn6+fdfBn03tdJiJssCAMpSrlM92Zfy3zRKYc4qFAwSgBQCDWsRgx/QY/XbcWiAfpgcmPwYxpi6esF5nLryCQwy8qqHr3gcAWhFqDdqN+OpeNxxJUoc7XXbsdrnY2VkB3u98OUDK8hMUKXHw07288WGkffjQyPuI1+o919yL9aVXhgAjP39Uj2m62TvQEChz1+iaTCC6Qd6aMR9aPaJ9GXH6Afxv5962FHHDKBw/Cy4TMb/Pr1B/0P3ZscddyG/DP/dEp1bZKkInAH0d2bke0aWKnrAVMFJpCoj3y8IEoAUCHr3CIAXHyVfZu2hKtaYbu+5c67xixC+CDQovIhshiTuskBz36QtOIRp0D0w+TG28X56WZljmyT/vpdcbm/6uloHP/fxoKj0eOD7Yx8iT7IPET6MRM5L9Pblv3V2Aie7eecnnzgye+R2hv9uic5lMv5q4G04qWw5eS7LPR7LPUC9v8vGLgoOY6DHA4OZZ98lfj8rYJGq7PryYcPYsf9x2Rh+Q1VwIggpIAFIoXDtGmQfDA1rMVD+Mbvp+FJ3ybUWO15EclX1+OcTFmoicnDKVgRwkf8DAEwMBGDZR3ew0ruUutOmUWsxoJBazifPjHvY0o5d4vOdP18AcIFx6Gtnz7LbRXUdHhpxH56Oxcnj9tNxD2NnPA53fXx3v7F9k7ZgtddLKuoJ9vGCIQaSKiOZAFslaHA8xYBFcCZDvV6w08+W6ltcJaf7N5Wr2GvJ5xMZXiEzSABSQARK29kJpo+ZtEan7cRqn88dngACAKizIMenbkNO2NOHCE6dnFM8M+5hfJMRUXh+/AbsjMfJIOSFCRvxv3Wsc+T1/NbZTex+maaSfb1q3pm0Bc1+iOlEjwcAwFcP3qb/n88zP8+MexhnBIPkvytXTGwCmgaDXOYd43RiYC8weIGR7k01KyK4j5cnPopLL7qIHFPJNnNqmByvKhYzVGWsExl/IkFIFQlACojwtJ3IyTp2XbUDmwYMIMdenvgoUs2rgnMJahqrksRlSPZN2oJBj8dVsrxBTWOD4yFeL4zw+8ntTsRi4MS+hnAiAf+jcz25Xz+8dB2+H35Mp+7TwV4v/K/TG/QGJrAY4vVeULb1cqiLbOJePfxuPBaJkLLNz4/fgM+MexglC5IbVOaZIpFevDzwyT36Pwz5Hhlocvc/ALCmoxyqxQZOPRMAYAzzzBWEVJEApMDwIgJlWAcAENZ1djJ3IBxma9SLHSdOyN6ZtAW9iKS61eErHmdNKH8zYaOrZHlfmLARJ5eWApXRuPmiO7EzHgdKRermi+7Ed8+dgwc+ucdR5+7PK1biCUbhCwBgyZEjQBkQrh5+N/76zBn4uypaZvOvBwyAn/Yq8dox+kGcUV4OlM/H98c+hMN8Xra8LdWV1Iyzp7glOL/y3kq9JN/nQMgbd1x8D5vl4sQsAAAqUzS1fPXyTexnccHJso/u0CUAETKFPOUKjA0j70fOQfnJMQ+y3h8nYjExGGLYfdkjrix7UJXjXemiXpCHRtyHnYyMLLdSCGC8yJ89dSpr+2WXfxjyPeSC/QqPBzxMmdyHkQgbNHxKBDV/V7UaOc3+HaMfRO5+f/Ps2fyWZIZq8vbVmeaUItgMT9tJnugTsRgMZ3pAhMKHUvizArfoZIe1l1xC/j30ud+QIGQCuZIKkImBAOsJEU4k4CvvrSRNCw+Ew6xniOA8Xr18E5sFeaN2MwYQyXO9b9IW1wVUP+/sZCfFMV2Hf2l/gBw7EYs5Uh1ohN8P/3nmkX77tfyii+A2Rp3mRCwGoUSin8rVVw/epnMNpcciEWUwQY0dnboNP4xE8uYdM/biN/PxtRmn4u3Fet9+HhPVsR3h95MSzEJxwCkZJoMrsebgzEkBADZ/+in595bBgwtGHlvIPxKAFCAvT3yUdUt99fJN7GryqShChT/7LqdC5ojpOvvCqlSspsfAXYpYz4/fgAC0O/jK6jX4ow5aAe7o1G040IGN1RtG3o/HCRfsX54+DX818LZ+J+3q/cv1mK7D98c+hB9HLtzOiwhHmWzGhpH3YziRIAON58dvwFAiQU6G903agvkywlOJLLgJlWcL17sEYH8CKhQGsysr2TFVObCqdIti9fC72X/P9YRyz1lBsIMEIAVKTUkJmwXZe+4c7QtSv90o0ZIsiGt49fJNWK5pZP/OCxM2ok+RBUmtYjj/HAiH2Qndhz09rOFbXNcd2cczb/DtF5RifeW9lRc0oN965MJeCNMB/WCf3+pFVJYJVnq90EYEOwBGVoUr03rnbFTZKO0odj/tuP2s9HigvW47eV5+qpBklhr74uaWYXex18bV5eUZ+x7OmPQ/zzyi/9PQO8h9+OXp0yKQIGQMCUAKlB9eug7bQ7RjafTUFHY7PXwRQIxX2hCcx+7LHsGBXnqVP5xIAGfoN7m0lAxOnMrBKVuxXNNIBafDVzyOMV0nX44Hp2zFHl23XVudTZ4Z9zD+5efnwIN4gSnhplFr0cz4hHUd3vm8dO7A5Mcw1ktQ4rUkxos/HfcwHurpIRck9k3agjFdp8vU6rdjqaaxCxlO4m9r9ud7F/rR28slFa5jlAyF4kAl1c3JqPfEn9DfDz+W0n36fxkZ3ofa2thn5Qifz7EZytdCtBGz4FwkAClkIowD+sylhmkh5YDesBZBk2Z0Dq7XAADgfxOKQ7nCB0jW9Jp+AlTz8g8vXYdBj4dtbHYiuy97BLkSFdWLMabrjl1ZNuUzzxIlT+ZvndxHe7/3b1WV85icuvIJ9CKSAVrXVTvYcqtTVz6BMT13l0fF24ttfdlzNesdOSmyw38Z9N2C+S1C6kwrv1UprEH9/VBPD4wLfDul64ZbmDodjwP3PFg0ZEgqX5FTZgRXyH3jMiQAKWQaV7AO6AHfORheQt+vgZLTjmzczRUPfHKPznlIHAiHSX8FAICfdp5iJZCzzdbRD+DljEHUM+MexsmlpeTYMJ8XOGU0p9IVjyu9TKhSrJcnPoqhRMKRpVjPj99wPgvSF1Mqkwq6QokETPnDLbrVACGACEGmH8aLyLpsV3o8bHlbpumKFI7JmarJl5JHBgCRQhf6iUxYoeRQN8SiT6S0HRfMVPt8MK2c7gH5xwF0VYUTcJO/lWAgAUiBM8jrBXyztd+NGZ62E6u9XlKOlZOHLBbuuPgePKZo6l08dCg5tmP0g8g5VeeCSo+HndicisdJ1aUdox9E1XZOZPdlj2BM18km+sNXPI5cn8hzNeuRyjI4AS/wMprLPrpDp1YrfzNhIwYQLctvmv4wVBDWXrcdK71esvTi8BWPowcRuCCJYuK737Z3PUWcO8FJlSsVz4K/JoQGAIzFgqztkOAKSpGflv2fro3kfVVzxffQ6/um5Wvna+9/V/8aE+zWlZbCjtEPkp/10ElaHStXqBT93qjdLPeOy5AApMA5PnWb8qYcxujN33LRRcoVvEJniaIR8KWuLrYU67WzZ+EfD9+el+P2FwO+g7tPnyZX0FYPv5tVito0ai0+feJE1vcvk/DuCgDVXi87WQ4g5tfjgmFMSQlc4qfvxQPhMPyRaRQPejzny+ys8PLER3EkU7K1b9IW1sByjN+fktNyhV2vgECbve0cCBdk2OGuj+9OOZsiuJO//i3tQv6vJx7UOe8erqGc46fjHkZOCOGvBw5kt8t3ttxUQxQKAwlAioAJgQA5IXtn0hbkDK9WD78b4zms/XYT/zT0DjZD8v2xD7GTuFygekGE4nE2ODoVjztyYs5heplQWZAXJmzEaq+3n4rU+TGfD/71xIOO+q1mGRV1fpYPG8aqXE0vK4Nbj9yppxL0PjnmQeR8KCo9HnKyu/uyR7DS4wFu1bQvqtVIrqk98NbNesBDS4S7jlf6CyUkY9A732S3OcQEoACZDXSE/NO04FHyfIYTCbY3I1Xu+vhu/V4mqH1Pca1xanqCYAcJQIqAA5MfY19Q3x/7EHL9Dq8xKhkCwN2X3Mse002j1uZtQvDkmAfRB0j2ojwz7mEMaho5yd192SNs6ZJTUUkJjykpYUuT6kpL4VBPT/Z2zAbhRAJ2jH4QY7re7/y81NUFAP17Cja23adX+3ywadRatjGfYyLTL/TDS9dhHdMv9MNL1+GbaT4TpvzhFp2Tph1TUlIc5Z+MzLlq8eDXn18DFD63SCULaXHzRXciJ487lGko5yhXPOs/dNizUShc3DXjEGxTV1rKNk9/GInAxrb+alnPj9+Atx9bIy83l7F19APoYaZxJ2Ix4JqWA0gHLk4moGlkdu+hEffhh5EIfP2D1f3Glgy7C9uiUUc1/M763Hzsh5euwxLti5P3L+0P6Oaq5+rhd2PvUoveZVFPjnkQufpwiu+PfQg5uc+/q1qNXDbs8BWPYzKBCkom2aRaMVEqLSADvkGlfMDAqQxG43zmtF0hTJhLlTIhf6hKoinzUhV/W1nJLqKpFtc4zyBBsIMEIEXCplFrcfeZM+RYWzTKSvI50UVaSE4pakCl2J8Z9zCOKSkhX2Y/vHQdBhDBTUHnq5dvwmqvlwycvj/2IeSu6yfHPOiYjM+/tD+g91519MEX56CurAweGTHi/NjH0ej5xYJQIgE3X3Tn+cnCu+fOwbZP77d87vZ2d7OLEidiMVb5qi0aJYUteo9zqPpVOIUuN6Iqwxzkow+dT1F+VuHlsyMlBRS4CTw+xXl+9vHUzAFVjuaqMksJdoVM4ow3sJATZpSXkxO1h0bch6+eDZEPntXD70ZukiI4l02j1iI3obv/knvxU2aSGPR4WLMrp+JFPO8S3pdhPh8raznE63WE0IIXEXorq20d/QCa5+ASnw9KPF+o26ysXnO+DOqXp09f8DlhXYdnOzstf6/Zy0SpYr16+SYMJRKkE7o+fReWILK9HKZ0cKoU0uRmTEkJ+feJ735b5wLfACLbB7L0oovY7yqKsjVB2f8xd2lq5oCqEixVMdf0DDqxC4IEIEXEk2MeZP0gPo3G2PKIA+GwqxqUBYPOeJxd4Q5odIbkoRH3oducmH946ToM6zobQA/1+siszoaR92OFppHlh7nkWCQCg31LLphA3H3JvfjAJ/fo/0Kokz0z7mH81xMP6jV9Jrm3DLsLg5qWUmnZM+MeRq7p9FSM1xob6vXC4BTrzpNhN3ABANj9I+e43P/riQd1LisxvqSkn6kkgNGA70WEU1c+0W/D/9O1UX9oxH3kB/6he7NjfreQPipxDM63ww4qU1ZVJpLrHRMEO0gAUmR82NMDOz9b2+8h91zNeuRWN54fvwFTkeAUnMH9l9yLJ5hJ5EMj7mPP918M+A7mS0rYLs/VrGcb0mO6zq4ehnUduGOUK7hynQ8jEfAwE9mGYBDmDhrU7+/Pj9+AoUQipSDEi0g6kIen7cQxJSWkN8jRqdvwbDzOZkFyDacclA9qSkrgG4xjdNOAAWQZmg8RuMzIr5nSWQCASWXLHfO7hfT5hyHfy8n5VPkhcSIUAMBmmnOFqvRTcB8yqywyJpeWsiscxyIRVm9+VmUl/FuKWuNC/lFNRtuiUVY//kQsBl9JwXjOCXgRSYWvh0bch53xOHkcVlavwWORCDzwyT15+a1feW+l/lIoRI61RaPwVjftCbD6449h86e0KZgXMSW9/oNTtqKXKak6MPkxHOHzkY3lR6duwxGMjHeqcC7sbuQnHR3wd1WryYlkF3Negh4P67OiagpWNfwLAgenpgVgzBE4ZgSDWdmf3lALHib69F3sfts2PxXyhgQgRcY/Db2DzWbccfE9yNX//9fBt+Pjn32WzV0TssBPxz2MNYEA2fvzT0PvwH9jmhGfH7/BMU3aVvnhpevQiwjLPrqDbEgf6KUD7x9euo7tl8k2bbEYqyxzIByGA+EwjP19/wbTfefOwZ5QiBx7rmY9vnvuXEqTei8iOwFui8WUjeVXEl4sqcKJBaQDp/KVbbaOfoCdJL3PSJwen7qNFUxQ0ZCDCaHgfFIRn0iGSulK5UeTKez6aIl8sPtw1wxDyAj/88xp1pBuhN8PW5iH2ayBA8nJneBsjkUi0MmkzrmsAYBRSpKqw26+UTXQc70gAADDvF72us8mY/z+86aKfakrK4N9k7bg5NLSfnLCw30+OHzF48g1Ox++4nEcoajz7kt73Xas9vnIYOI3EzZiOJEgVybfmbQFawIB9hqyyrEsGJxNK79VWc5ClaKaqFZhk/GfZx6xpSKkqsvnWFm9RkqwhJSh5MlNVNlTzok9k1ClpSZchUbF24t1lUqY4EwkAClCdox+EAGALEn5hyHfY7Mgd1x8D1ZnqORCyB3fH/sQeoE+38/VrMcSDcmG9HwaKtpl6+gHsNLrJRvL77/kXgwgkoHGvMG347lEIucO6dPLytgxs7zp+fEbLjAa/NtDq3Rzrfw3ClnbVy/fhFady81/H/R4yLKedyZtwb5N7yY/Hfdw2sIFswYOTGt7O/SWMKag/GWs8KrCrJHrRbp6/3JdPBYEuywZdlfGntWc2ejX3v+unosARNUHcz9TjhjQNOi6aofr3lfFjgQgRYpK0m9MSQm7ipdJJQ4hdzwz7mE2eBzjL4FKpvzj42iUlbF1Kvdfci/rDH73Jfcit9I8xu+HTPU0WIXbz9uO3qX3zmDEdP189maYz3vB+fra+9/VucxOpcfDrhpSDPF62RKIznicVcPrjMfhX9ofsHWdfPXgbfqbTK9Lvnh54qM4xOtVeiJwfBihS0Gu3r+cncCNKSmBd5hMmFA8pHKvpkPYhuT1iSSlmJmC8x8C4BcFRCTHnchZK1KeGfcwPtfZSer8//XA23Cz9HsUHGNKSuB0bDspVesFgP/RuZ5s0h7jL8lbPb1dhnm97D4P8XpJ+dJ5g2/Hzng8Zw3pTX/6js690AOadsEiwQ8vXYfTy8rg1iN36l8qD8JPxz18frL6U0Vw+eSYB1GlotSX52rWI1eG+VzNevQikhmSW4bdhW90nyV7jZKx+8wZtgwtF7x3bgu5zz8d9zD2Ps5W+XKQzgaFFWpsggDAr/BnGi6bqaIEUekfkikefZPvAeEyviWaJk3oLkQCkCLGhwhfYpoYn6tZj5zk3eKPvqe7TaZVMCaJx5gV2CXD7mJLbL5UXg5uEyCYN/h2fPJEO9mb8OcVK3FHezu53d9VrUauXybTDPV62Yblvd3d/bKN7547B3vPnYNnCOGAl7q62LruE7FYSi/n+y+5l5U0fq5mPXIZEh/wjewq8u0tMKH0loxO+v7r4NvJz/MCsFkOzoNJEHLJbUfv0rnFjOE+HwzNwXX61X/czN6PnGfTUK8XDkx+TDKILkMCkCKm66od+JvT59hxTmFlx+gH0WklE4I1Xjt7lm2+ffrkSbj1yJ39xv5iwHfQjS7VcZ1XVwp6PGymo9LjUTYoZ4ofXrqOfWFSPRF3X3IvliBCD3EunqtZj3vPnYOr9y/vN3hwyla8PMVJ/qGeHrax/FgkQpZCbB39AL7U1aUsoaBww+R73fF7Lf0mVXMvJxgAoG78FYRMwy0UVPt8bHbEi6h8ZuWC15j+FLvKWYIg5Juf7yZfml89eJvOTSau3r9cF3d0d/Ja6FH2vKlqkHMxKc80Pzn5kM7tN1WOZvJRz9as/1Y7Dux///53da4v4S/fW6mrVOpSbbD/+ger2d6Sfzx8u85Jf175h1vY50Zf/vPMI7rbeoxUqMr3VOaQ//v0hoI5BoLzUT37nEzTn77jyv0WBIHjtf6rpufZ039F3CRf5m1C+lhd0XUav7PRi5Krxs5M8S/tD+ic6edfvrdS/9Ifb9W/9Mdb2d/EBZhfPXibnqq5JJURSzY28d1v61ZLvnZ+tlbPtfJYNlEFU7JgIwjZoelP39HFlNN9SAmWAKBFAH7xC/rmbWsCeHYPrYj1+gyAl9bJTe9CWgYPtuXxoSoxyQVTk3g7UITicXay/MAn9+hcgLLu+L16toJs1Wr4L0+fZvsIAAy53JqSEtaT5793dpLN4M+P35Cy4WJQUY5W6fEAFSgdmPwYliLCFAsGhW92dytlN93GUC9dP7+x7T6dkxq207gvCIVKT/wJ8n5QZYx9iPBczfqCeY4UCxKACAD12xECbfTY3AaEuQ30jV12xAheBNdR7V+KT588yY7HEzvIh70XEW47eldeJ0wPfHIPW1ZFsWnUWraH5Y6L70FOenf18LvRkyVzK05+F0BtSGf2S4zw+6GG6es4EYux24/w+UjPF477L7kXw7pOZpHuvuRefPPsWVKq9p1JW9ALkLT5vdDkM7luFlWNuso8UxCKjXVt9FzkzbNn2YWb58dvkJvIhRTW01+wTWDwWzD8d0vIm/tf2h/Q/xdVo3x9K0KkSrIgLqVUMfn7UUcH6QXz/bEP4Z3Dh2d1v5Jxx8X3oMotl2Lr6AeQe3mpDOl8jHFhukwqLWXH6hhzwr9//7u6Gbg8NOI+DGoamcV6csyDOLuykpQh3jr6AawJBFLqP7ll2F3Iycce6umBTibgiYFx/FRByGTFcXAbd318t348Rssqv9F9lr3OuPMtCMUIJxyiIt+ZecEeEoAIAAAQnrYTTzETiX8aege+FgrRGwbagM2eCI7muZr1+H+6NpIP7rrSUnj3HK2QNtD7LVQ1sueCwb4lmGo/iB2loZXVa3ByFmRiTzL32n+eeUTnXsABTbsgc/JPQ+/ArzGB2N9UrkIuu9AWjab8kj8WiZA9DM+P34ATAwEyq2J6e5QyK/xfeW+l/vPOzpT2w8l4wVAIpMa4vwMY5zFrOyUUBP/x49R6t9wMlx2eXFoKzzC+POUemcq6ETlrwnnCPQMBfv04+aDbzZmZNazFr1+UXx1/wT4xXSfr+KeW34oqt9zhOXYMp3j65EllL0Vfnh+/Ae2slP3VwNsyOkH8ycmHdG7l34sIf1O5ivy+cCIB3x/70AVjHm0xG0ROKL2FDBRXVq/BVPsunhn3MI7x+4FSxdo6+gGcEAiQmaIAIjuhGOH3K0vRckEmzcvuVpjIqZr5BSEZ18+nDfgKkalMRjDo8bBKfmfjImPtRiQAEb7gmvUIMdr749XLNyH3sv5RRwfAb1OXFBXyz18M+A5yPQcrq9cg1wsyumQZ5qoXhGu2botGU57A5lvHHsAwSeQCAFX/BtdA/ucVK9nf9H5PD/t59358NyuzSzHE6yU9Av7QvVkv1TRln8OXD6zo9z2VHk/eG0cPTH4MudLTTLJp1NqUf2e+e60EoTe5UhPknlnvnjsHnJGuE57rQupIACJcwFfG7iMnCwAAFZpGrhjq03chlh2DoXu/JS9MF/Lzzk62J+BQTw8rfZvJ1eufnHxI/28ddC8RVy70zLiHcXp5OXB+FG7kvwz6LnlQ1x2/l3UoVqFS04pBaufw7kvuxesqKvr1nfy6qwuORSJwPBrtFyx6EeGN2s1Y6fH0y1b9v1VVlr87mxyfui0jF/Jru3kPFjtsGHk/u1//o3N9wVzzQm5ReWn43rqZHbtfkeHLFF/64636jvZ2cqxpwAB2EaaQvIQEobhhjAkB+JKFKX+4JWWPAcE5qORmqWZmE1KcIMdwgUumyafW/Pvhx/T3w4/l/VgDGOVEvZWveveGfOW9lXrvMrfe/67vCmquzlu6jP390qztJ+fnQjnN9x5zy7ETnIfKJDSTJYl2mPjut3VuAVRVbiuLn+5EMiBCf7QI2wty4LMxAK/0n4Ttm7QFO+NxckxwPndcfA9yk+u2KK3sAwBQU1Jiy0/EDlyQ9HdVq3OSft992SPYMnhwLr6qH+MC38ZxgW8rf+fOz9ay8sSZVPI61NMDvVWxAr2a3ScGAheUafVu4G+LRs+r1dx29C79Q6acwmkcvuJxTObsrjKG5Lh6/3KdE0Y4EYuxk8HOWCxn17xQeCy96CJ2zKfIiFrx9UmXAyENXu6k74k9nBAOAAy2oZwl5B8JQIT+NDcjVv6eHPINfA8CJafJsTc6ggDh6mzumZBFDvX0AJXF+pvKVcityI4LfBv/raMj+zsHRs/ARz1byf1Y9tEdeioN6QDANjQCABtUBTQNuKbvfHPzRXciN6G9ZdhdyPXSpMrz4zfgEK/3vFdMb4O9raMfwLuqv5BpnjlgwPn/f3LMgxjQNFh3/F49oGnQGY9nYndywsEpW5UT/lcv38SOc0aDEwMB4BTWqr1eODD5MfIzK2WyJaTBOYUa4HeGDWPHQrm4X7UI6y129Gw5u9mBbr53TnAuEoAIJHrCC/hma78XZ3TaTgzHPQB7CFWXhrUInzTnZP+EzLNv0hYcF+jfZAwA8JsJG5FbRZ85YIByMp8plgy7C0eXLCMnZW2xWMoyu/8w5HvINWF/zGR9/nrgbahq+s43K6vXsPu2dfQDGdvv7499CH/U0UH2Dmmexfi/T2/Qd362Vt/b3X3B2JNjHsSziQR4ITc15U6AMxqcVFrKKv6MIZr9Tf66V1AnCKnSo3hOcuIY/3j4dp3rv8gkQ/0Ag3zMq0RheoyaBCBuRAIQgaZ+O7INqmdqATrr6LFIFcBTHY5cIRaS87XKQWzT+TCvl6zbv2XYXRjUNNK4MNP85Xsr9av3L+/3PT8d9zC+dvZsyp/HuaCrJvKCwYefq9VQk5a/GngbBj0eWDLsrn5jAcSCW8VXyTtzgV+FpsHq4XeTY9dVVLDf9aVyfiVYEJKhkovmmF5WpsyOZIrOeJxfSErQao0AAHqYLysTnIsEIAJLtdcL8CJhODdzKcKZWoCn2/qPLaySiZuL+auBt+GPmJKqeYNvR86cMKBpkIua/t9M2Ihv1G4mr7H2uu1IBScqGoLBnPWwAAB8zJSQOQWVQk5f7r7kXjwQDsM/Hr6dVlALh0npzjsuvgdnV1amsZfZJ1XfjgBj+njrkTt17rNUkstvMsH0Vw/epj9zKjclj0Jhkor0tsmSYXel7B1kh5iuQ4zzn4rwqnm+Ulo5S3A2EoAILEdV8pSVewEu3cVvLFkQ1/Jmdzdbtx7WddL1+m8qV+GHPT0ZlcS1o8hSU1KS0gt2avmtOG/w7fgv7Q/k5Hq9hCkhyyVX/uEW/UqmoXSo15uS3n9nPH5BQ3pvJgQCMJHocfjvpx7W3z13js20OYGApqXkwzGOKZnq617fG1XRCBfQhBMJeD/Me7sIQjIagrTXVzK4hYZMoie8oCfo7CgGP2C3c/qChkAjAYigZPzQw7TCyw3zceiANiBNvBZWsYaGgvP5zYSN+E9DhpBjD424D0uYydHdl9yLmSytOalYIeZ4ZtzDyNXcq6hjavG/evA2dgUbwJjMp/xleeadSVvwnUlbyIP0w0vXYaq9GaFEAigFtXmDb8fXQqF+Gv1/DIfhUE8PfBiJ5DT7lAoPjbgPVepvfeFKqVoGD2b9PMqZ++iuj+/WOe+bFyZsxBcmFI8rtpB5umw0ky/76A69RMvBZRcLAtRvJ79IFTgdCoeztkuCIOSRQe98k5wkDHrnm3qAMy56ZZUukrzu5f3wY/p/P/Uwef7+9cSDrA/Blk/v19cdv9eV592O8/TY3y/Vk0m0pkO6ilsjs+jy/a8nHtTNvp9lH92hc8fvHw/fznoEUdm0YmDnZ2vZ++T2Y2tYh3ouMykIBQFV8m3yW9osN+l2gmORDIiQlM5XHgPv5u5+N/ipK5/A2MubyDG4Zj36GLlewfmMC3wb/xgOk47L/zDke/hC1xnSGOqWYXfhcBuO3XbJpCGbHS35w1c8jpVpqsPs/GytzmUC0lXcUpZRpsne7m448PnK49bRDyB3/Drjcbbc6O5L7mXV1dzC1xTyz1w529SyMvh/GDWrGeXl8NCI+8jzdtQl3imCkHFUVRV+6YtyIxKACEnRl/gwVpb65CzxymPg2drj6slFMVOhaawJ4elYHMJMs+DxaBRylQU5lkKZTDK4MppkeBGTmnTd+/HdOiVZC2CUMM0bfLvjymr+LUl51Ai/H26+6M7z+/1WdzdZJ/5czXo8Ho2y7t4/7+xUujM7naVDh7JjnHfCSJ8PppbfSp7zl7q62M+7nPENEYSCQCG1e+Wwj9ixpoukBMuNSAAiWKN6N8CPDvWbJMSWl6HOrADHl5VgvDx1aVTBGSwZdhfGdJ3Mgjwz7mEMxeNkf9Dq4XfjpxkMDACMxmXq77cQMq/pwAUJKl69fBNekiTrU1dWxkr71pWWpvqVOSGoacoejb6KaD8d9zByyk4HJj+GLYMHk+pSv5mwMe0sUj75iwHfYa9BqgkfAOBAOAxn40+Qx1Z1LFhpdEEoBLy827nq2pfA3J1IACJY40QDOxRfVoJsv0fwEMBP9rp2dbPYufmiO9lJZVjXoZqZeG8atRZVpSmpkivX7GT+H1wvw5eDQXaFHwDgvwz6Lvu5fzXwtrzOKrn9/i+DvotnFaZlI/z9dfmfH78BOT+YE7EYm1GbUV4OmXJqzzUqkYLeGaLetMdiUO75ZkpO54s/+p7OKY4JQiEwZRCf/VMZIR7qEWU4NyIBiGCNhVWIE9fTDugAUBE4A2N/v7T/2I316Bv/RNZ3T8geoXiclNd9eeKj2DJ4MJs1aFOU3aRKLjTorfBmdzdQwgt3XPz/s/fu8VHW177/WjPPXJJMyIUEggQIEkwgIMEgB220SNFy3GqtZfdn3drqaatUUAREg9zkfjPcQYJnH93btnqqrW7t3ocWK7bQ6hYtWFCDoAk3E0wggUySycwz8/39MUyYyaz1TWYyM5lJvu/Xa7+2zZdn5pmZZ+ZZ67vW+nyeRZlKC/cehaPJH2n2SVSVfpq9kF3j1LK+3e9JpIbn5+UswTyLhZyL2DZsFRYnJ0OizYM88FWZ4BJ02WtJlQRTExhFtiQ0sPK8CkVvgKsYAvDfCwCA6RkZ0TgdRZRRv2aKLiMm7EZuh2Kw2cyWSF2t2QC/fz2hAgvFFWYOXMxWQb6X8TRrTri/cCNyPfDxTsU3K8nrtWH889jGzL6sGLwMuYSC+25ww8bRgguKWTU7APj+iadYVSaOPZcuke15a3KX456LF8mZj59mL8QDdr4FIx5JNxrZlqlGiYy0jUkkZpx8Rrx0/jx7HCfpq1BEE0pmOxrkSIRAOKn0+acXi2pVAUlIVAKiCAm72w2pf58R9GNUOWYrGrkezdKVmHrVO9E+NUUUOeNyse1HR1tb2SHiE21t0uA2XjnU2sK2BFkQWWlqu9tNVn0iPasSLpzDtqOkAjnjxzfy12OorT/vNTWxjsYHR29GLpho1PW4v16+dGxtP7+709Nh27BV5Ge7lKkQLT67VLzDDJrnmS3wQt5q8jiq5U2hCAWZ7LXMaHCgKXL+TjJ+xvhPAcjNBjlBFEV8oxIQRUhUOTzSMikXiOaYTAmtdNPXeSFvNVY6HGTyeXD0Zjb5bBj/fFwE3qGya9hqPNzaCtd/9kTQ63WUVOBAZvZl27BVKNv5DoVIta/5k2I0sF4SlWO2sp/V6w0NbNJFsb9wIzZ5POT14ns8am5sb0E5OtxG9rh4YIT18fb3qeP8zsm2be3n/cdf059frcsFeUwy8SlTTQRQA+iK7sMZXALIE9wkjE2oeFRiKPjqBVpq9460NJiamhqtU1JEEZWAKEJj4k5sdLsh+/CjZBWEG0o+PnYb28ajSAzqdJ3daWrzeGAQY3qXZDDEdUDJsb9wIx4cvZmM+irHbEVOejfXbIZpXzx5Ze3D4O8KAJDfIR83fD5HyGYzwmXXsNXSWRWO0+N2YENrKjsDRlE5Zis2tdA7mmLCbt4naOJOlG1yxDPDLI+1f2YGY/DH92/1q0W60cjO1siSDJk8r0LRFTiBAwCAf0pLY9emSdYiyTuXLrFr3KB5jcsFhyWJuyJ+UQmIImQa3W6oc9C7JQ6PB26qnMv6HVA7yorEwFFSgQAA+NHDQZ9h1bXb0eHxkLvkDeOfR5mCSaJS63KRr/ftkRsQ4IpiFhroxJtrUQLwSvtG5iyD+UnWMzjjZBiO2p7QW4CsSXXsdz7daGT9UxxCwPhOvFXimfLa5cKSHLwZ81FzszTJ4CojAMAqiCkUXUX2myNLcL8bI6W+ezMz2TVuU+Le/guQm6lSxDfqU1OETF3xTgSPGeCd7WQ7DjcQ5iqpQKXikti4SipQONPZde4GpwGwFZJ4h1R3A+/3gGvF2nPNc+jbleN6l7ljY8GuYauRSwxuPTZPUAPkULrS+73/S9e9UhwlFZhuNMKdx+cHHVNXvBOnpqaSszZHiragzWhM2GvGJQTcdOeaoKBt27BVaJUkINwO9WMnF4pwKlcKhT8yNUGT5LqM1RC6LMmuZNqzfnthreA6LxTxjYoGFeHx9V1gzThCLukAAO+tJX+wsjQtKr3tihii2QE+CA5eG8Y/jw4hyArJ6XE7sEHXwRTnA8YUVdduRy7oHmQysRW/uvrRAO9sF2/kryfv7LIbfiyodjrJCs7egnKsdbnoxOvmJSgzC6PYW1CO3K7/pqEr8fcX6Vas/YUbUVYRiGfKBi1lP1xuAP1HXz4tPnfQrST1uq4UsBRRxSWpjsgGwCOJrDrIre25dAnebGyM0hkpoolKQBThMb0UHa3ZZEBZM24HwoWJ5GFv5K/Hfedi00+qiBITL1fACFwlFSg8sVFMiSk30waF+wrKcX/hRvrOOHWWNGA843RG4MTCp654p/T8ZAOr3AYDx5uNjWRiCuBVWBvCVDrqdZ1t00pEbqqcKzhTzaMOB/zVTquU9cYWRkXs4TZLAORJc6x8mDafO0f+fdoXTwru99Lh8SiBhgRFJSCKsLEm1fFBSvIpdvgWPGZyB12RQDhyAN7cw3++hMKRo6QCXW5jSC08nSFt0SHOoVtIgm6qkjDkk5li7KDjtHrUuxtFQ9Mg+sEifd4S0o1GstJRde12PMPMuMCN670J6Lsbu3yeYsJuzDObyUTDVVKBOSYTOfNxfOw2zDObpW7jPYWsLYVTGsszm2EM08uepWlwpGgLGUkpp2dFJGA3S2S8t1aEuuEQLk2M3Pfk1FQYk5RErt3fvz/rq6OIb9SnpggbR0kFsgnItAcxm+uemFyG8PVdUTsvRQyYMhfBWksH5ZM2sxUSmLgTQY9cL3tNqwlgz0vkzTEjqYlNdMd/Oltwsx0sHjO7i9/q8QQ9l93jAYfHAw114wH+69eBx5kvsFUVuDQ6pNPqDlXXbseqxgHkPFfNuB3YcL6I/oynzEXsVxnyc3FrB0dvZtu0ckwm4KoGPYls19V/zqO89krCnW+xANevLms3kxm0KRRRZXIZwuSymJQYKi/QqnlnnU443kbPgNjdbtg0dKUqgSQgKgFRdIvqtjZ2SBdA4rDc77OY7vQqooD5AoCTUS2RDSs7M9mkIWQmbUZIPkU+V8P455E7v0NFW7AmVFWhKXNxMqM37yipwI6JQ8O5/wHHx26jEy5HDvs0mPW30M6ruzhyAAxMO5j5AmSnnyKXipOSpC0dFKU2G5vEpRtp/48X8lbjRy0tcTeQzpkzzjj5jPCXq56XcyXR1CGwnWrV18+KVV8/KwAAXhwePLTuwx6iEaRCQfHq+TXkd+hf61aJHedWkGuhfse7BfN7fcBuh/9m2hMfrK6O4gkpoolKQBTdoum6XexNs654JzrcTO/y1FmI5sYonZUiJkyZ6w3wqR1y3+7+H3cHr017EGUBeMh4zAD2fH6d8a5wA69wxVHjcvGtZ87MwDXfzfT2+7zvxVtvXVlrGUo/wVtvCfHN5FBOqfvc9rA3iaOqRTcvwTyzmRSOOFS0Bfdf0EISFnhlxDpMNxrJoGZvQTlyIhVHirbgGKs10F+lB7nz+Pwg5Z0FZ5YIAAATYFBysuzsUrG2ZpnINZlghZ9D+sKrnkWfchzXZnbLsXnio5aWCL8CRV9kFNP+99PshThz4GLyXj4hOTmq5+RPauZR8u+HTo+HQ2eLyLWmxmuieUqKKKISEEW3qbIns1r/I5OMQEp6gnfHM5LzAIoeIPkUXwWx5/M7/c5MgN98GJnPfnKZN6H53bt0IsQE+66SCqwOcRC8csxWb3uZfzLh447pCJkfXgnIMz+8snb7fe3qUVwFAAC8lYgw/Da6zaTN3koNkTAeHL2ZF464cT2mdzIgPePkM8Lfd6Rh/POsGWLVtdsxl2lRshmNMJbpA481GmKQZOia3OW4tmaZyLda4eWr1wYEczoAnNd1oMxYq51OeKhqgeDaWU84HJCmhtAVEWBcyhwyyfh5deB31J+ncyK4WdQJ+RYLvdDvM++9hsJaG70TUkQVlYAous+N61l/j+Njt7EVkobxz6P68Uhwpsz1Kp79+rPgm9ft93kTg1cPB6/9cCKC7QQdyIfDPVP4uZPbHkYyOQHvcHTQfEZXnqvfZ/Sa+QK42tK87YXmC4FrmR8CfPCEEHoywF130d+LrAPeRKYncOTwN3PNLh06l7VHpRqMsGvY6oDXVGi1wrHWLeQxiwYNgk9bNgetvZG/HvtrWsBMRU+RbjS2G076M8hkIg3dVgxehjkmE4wgAqwXh69Bm8EAXDI8tV8/b+KrUESJLE0L+o72BIcYEQar5SKg9Rv6oAjOFCpii0pAFBFhf30y29d/tLWVbYOBlqEhS3oq4ox7i/kbl8fM7+i3DOVbkcLBY6aTHQBv0sBVXG6/D+H1A6Fdg0y1ACZt9ralOXK8A/fUGlcxApCvRZupsxAMTroqeeN6DEqoLlNXvBPtbjfbipVLDFf/JOsZ/P3Fi+1tS/4UJM3Gj1pa4LcXgn8XygYtxVqXCx6qWtCjvxkWA33J/yTrGdSFIKWD5+Uswf9ubibVvt6z2+GdS5fIGRjVfqWIBKu+flasrVlGfm9k5phnQ52ViwJuuLxZRNETFWNFRFAJiCIylK5kB36brtvF704regceM8AvTwTf3HzJCbU2vdR7XVDVk3D44UTvc3GJhm89EkydxV/Tk8uQHeo2OIE18uNkq2NJ6UrE5DPsGjcz03TdLrYV66Nmenh0Xs4SbHAHtyQBeAN5ql0JAEAXosd1/8+56HMD8EqGTiZazK7/7Anx/y5dglbC8M0lBNS4XJBHVEg4B2iFIhRGWCys18f0jAz2OHscKNDpEpNEFVskLioBUUSOAe9C9mEmiGospls4Jpd51YNC3YFWxBf35/MB+X2j5RWSSMIF/p0xvTT0iFa3ydW8qOu99LJcJDHwnW0GyEitCfk0Ik2+xQIjjzxGvq4sTWPXAGg/FJmE7vikZFh2din5eDkmE9x5fH7Q2qahK9FmMEh9OKINt2P8Hw3rRLrRCJNsNviPhnUB5zc5NRVuTU2FDKMRfMpXPs46neAoqcDBJlPQtSENvhSKLtLs8cC/1q3i5zEZDtiZDZN4QSUgCYtKQBSRY9Jm7M/p1d91Fx+gfjMF2J56ReIw4F15Ox1V6bg/HyHzQ4Dfvx65KsiAdyPyUJ1yx3TvYHlHD413N4r2vuSOSch7awXcuJ6sFja63d65qB7m+NhtOCYpiVSjOjh6MxoRyTaiuuKd2NAWuIN/w+dzBDUr4eORAYuw0e2GxUQS8r2Mp7GRae3aNHQlfsBUVqLNrcfmBcjs+lPrcoHd44F/6b8AbQZDQBJSkpwM24atwvdHbcITbW0BbVrFl5WG9lzzHA5KcgVs5HAD+wpFKNg9Hvhp9kLyu/hmY6P0uFgw//Ri9h5gkVU8w910UvQ4KgFRRJTKxn7svIcp4wjdN/8zmzeQo9YUicO0B4PlZn3cn+8dOueOi+QsSIxMswDgytyEP1PmIkydJe9XJpzEXc2Do3KK4fBG/nrkrO9SDYYALwt/UGsJ2MF/f9SmTj8LmYnYAbud3Z0dY7XG1qPgMjajkW0Ba3C7YfZlOdPvpM1H/+rPb/2CvBeHr8GfZV0xXfP3l9GFgMLLcqlzTi0SKgFRRAKupbGzNc6BPNLkWyxQ8c1K8vtMtSYqEh+VgCgiSykfTLhKKvhgxJEjNWdTJAj1pYHys/7IlJR0Gz9AHu94zHTyPGUusm1avjkR//cjznbyjIjt3hT//OXTwtfydHD0ZtSFINujxITdODxZZ2W5OTbU1sIQQklLTNiNk1JS4IGvykjfkOkZGfBv9atjet1oiPDaiHXkb1l2hwqwzWBoV/u6t0Of/eyBi/H7J54SN1XOFf7KWXXFOzFL02DLuRVCQ4Q1uct7vCqmSHzOSCTHuYoeAEAeISIRDR75Rzo8MmARea1X1iqvj96ISkAUkadlaHBbio9Lo+khYZ9PQqRkWXsxPdn73inTSzEj5TwZTMLt93nb8KiA/L7RvLRtvDN1Fu12DuCVAPZnytwr/1u3QUCrVpzJSe655jn83OFob7fy96l4f9Qm5GY7qq7djqHuWDpKKnBqv37ktf32yA1Yx+zQzh64GI+2tob0XN2FSz4AguddfpBZhq83NEB57XKybSvfYoHJqalBQd4b+esxS9OAbWlVKEJE5nl0SKK0dscJpnIdS7iKMkDgb6oioVAJiCLy3PYwH0xOL+VbcVQVpEu8kb8eKZnPeKFh/PPImaqBPZ83lEo+xSeu8Y5u4+dYPGaA//q1CPIb8Slp7XlJgDMztq1jXaRNCMizWEADgBfyAn0CrIjAmZflmc0hS+U2ut3swPqea55DMqkFgCOtreS8Sk9ADacvvOpZBIAg40IAgA1DVuCE5OQg40IAgNNOJyQx/koKRajsKyhnf1/2XPMc79fVMDI6J9QRWZIhIa435BRS1K+bIiqkah7WFwB0G10FuWcKqgSkaxxhDJvihUa3G0jJ1runoSnlLG1cN7kMod9npJJS3OMzFqQqeLffx8vyTnsQpV4pPcy+gnKsbmuDE21tQWt7C8qx2e2BW4ngf8OQFfheUxPc8PmcLn+Wb+Svx8MtLezvRqnNRrZ97bnmOSywWkhPkVgz8/L8R0eKk5LYXvrfX7xIvi6f50k8GC8q+jBx+tvk44389XF9L1TwqAREERWartuFroax9OJdd7GmZuAxA7xUq264CU7VtduRkw91lVRgTRvzEU/cibFSXYk4Mgdz3Rb3N3KOQ0VbMNdshp9XB1c7qJ17H9yguoyG8c+jLgRYiSTklRHrkOtj3zVsdcxUsWQ7rpzRm4YIDbpODtmmG41QaLWSa3a3G+p1nW5pVChiQZiViVDh5LgVvReVgCiihyOH90kY8C7dbvNgDoJjABi3cRGqIlGo03XAjx4mP8dsM5CuzwDeNha2ehbvGJx0FcSXnMjkhju2aMUJhUcfF41uN/gPSvuv7bfbyaqVSwjQhQg5eM41mWDUZRWojoxJSiKrBQAA1W1tvA9RhFh2dqnIl8y3cEZvbzY2wr39F+AZlysoSRmTlAQbhqzAjjMiN1XOFXubmmBN7nI8Ewdu1IrE5kdfPs1+N7i1xWeXilglBksHL2MTnT9fei4ufxsV3UMlIIroccd0Xl71Rr5salQ3216Bo6QCxaVCUvmqrpivdDRdtwtd7tB3z+OCO6Z7262oRENWIQGIOxUsH8ccDjjc0uKdYfhb4O5/5Zit6CipwIaLw4ISqOq2Njg4ejOebk4JOk7G6XE78ERbG+kM//LVa/G9piayQlJ17XbUEKVGid1l6eBluGHIipB3hH2yuna3G1I7zHVMvSzBqwsBW86taD/3af36wfWX/UFyTSYoPPq4CsIUYSNLYrmZvRSJ5HYsoVpAFYmPSkAU0cVj5pWtmFkQ92MWBABVBekNGJzs0LloyeWHzp2ZiTuQ3hkdk5Pb70OpQlgPI+pvhIbxz2PTdbv4DYWWoQDphwP+5PD3NQkxuWq6bheOTDKSMyRN1+3CVCYoqhm3AyelpIT0XNFm/KezxS8veFtONw1diTajsb0KMu2LJ8W9VVUAADAvZwkCXGnxqnW52lvcTo/bgZWXrJEz7FT0OVIYQYMZJ58RnK9NksHAet7EEs5AUZHYqAREEV2ml/ISpb4BXEXvZXIZgjOTHiy/eYnXEZwKuktXemWZExFfFYRqqZJVQXxJSLzhN69lyjgS0FbX3kZ3+32YkdTU3gI1/B+zxJC0c95/dON6tJpaSfd0GXlmM+SYTOSahsiKFeRZLD0yuM29vkPNLvCXCv5J1jN4zuWCH335tGgTAvYXbmy/JmYPXIw+U7jfX7wY+EA3rlciHYqwybPQvy3/bW+GA3b6t3ZtbW27sWbUkWw4kaIlioRHJSCK6GO+wBvQZX5I7uqpKkjvYVDaOVZeFQxOYGWZdRtt8JcI3H4ff9PuLAmJNy8cv8/HVVKBQk+G7MOPCuvHjwR4WzSMfx6tiJD69xmiXtcDzM0cJRW8NDPD3oJyzDObyWSiZtwOLLRayXmQFYOX4fE2R7uJYqw4xCjTmYxuaBj/fMDapqEr8YPmZtIc7mBzM8A720XVV3cEP5jBmbjfCUWP8l4TnWQcOt8fDtZnkms1FwdG85QCSM08yq7V1I2K2XkoYodKQBTR547p/M7dbQ8jNBazh3qUDn7CUzNuBwqPRvb0w7QHvYHZe2uD13wGf3E6nN0psiTEB9dyFSdJiPXjR4TVGJg8ppodUK/r4HAlgaukIuA1+gwDbUYj1BXvDFirdjpDHhLfNHQlbj53jpx/eH/UJvb9bfOIuOkb51rCqmqvheOnbwj6u6OkAuHSaCArx/dMwZGDjkA8+wAp4pD31orKC1nkkjXlLHtYYWZ9tM4oCJfEjZ1VzXxvrSDvHYqEQEV3itjgzAR49TD9Q5F+GOCDJ8gqiLC0AfyfC+oHJtGZuNMbVFGUruQT1DgdzO4INRTdzgFmJ55rObjrLt43JMY4vvypNyD2o+m6XaghkgGyo6QCrQYDOAiBgeNjt2G60ciqWHFYDQYwMX3o7zU1kSpbLw5fg2+f7R8XMzWcizvc9rA3wX79QPA5+lrxiOT7+Nht7W1aCkVXsKZWsYnGL7Kzve2wBA/27x/V8/Jn/GXBBYpU29f0gm6jE3VFQqASEEVsuHsaQt5L9Nod0xG+mUKvxUkgpogQXPtIv88A3txDV0jMF+IikJQxiJlVAACwJtXRC5o9aHA7gHgYOGZmUly+XXqCuuKd2NCaSq4dH7st5Fas42O34aSUFFIqtOm6XZhvtdLeHJPL0Jr9fsT6xznZaACvZC63VnmJlhSG33wowJFDv8fWWu93QjYHxSW2CkUHvp+eDt9PTyfXZN/Hi1zrbBRIk6htsdLXU2dhrHxKFJFHJSCK2OExk5UOAPAGYlRw+r8yEZzp0T0vRWyYOss7dE4hkWWGqbPQlP1+lE4qMqRLbp7czt7Y7BoYksRIY94xHcFa2/NBpmwo/vb7EH73Ln1+mp1NNut1HaZ98WRIr+uFvNVoZdox9xWUsyaE2ZpGVmPCoemLGeza/qP38Ad+fVfnD95RDVC3eSskl0aTiXnNuB0I9aXAvv8KhR+vHBsHr4xYR/7GftTSwh7X4I5dpS1J0m7NffcViY36VBWx48b1vIznzUsQaqeRS+h2A+5wqRttb0Czy3f2mdkHV/PguB6+zTXTgfqtx+YJDREeqloQdO45mganx+1Adlg6+VTPVwA7U+W6ZwoGBchvvSVg4k5vWx3xeb49cgP+lVHdkXG0lVfSqtd10rzy9Lgd2HC+KDIzNVwfOgD/Pv3mQ8GupR/2PuYPJyL8cGJAcJg69E3vf0wvRcz9HX383dPiplVPEefY89mlILU1Py7qsauA6JIZEFl1RJG4qAREEVsuTJTv2r1UG7QmZppQpNZE9bQUMWLag96+d6ql6u5p3nYrKtG4eQlKA8AeZOSRxwSlZgQAkK5poCFCvsUS5IC9t6AcAbyD1uTBN65Hk4UPDqLOv5/ig2fZd9gXFN91F1vVarpuF189YTg4ejNaDYYAGWAfrpIKdNXcSicaU2d5Nz662caXmvcb8u/Zhx8VkPkhfVDeS2yb3fjBn3qveQL/gVwxYTeyFaO7p2FctOop4hvJRoKr5lZ27ajDEY2zoc9DkoDsueY51WbVC1EJiCK2TC/l/R08Zn6gLPkUPSOgSDw0O9/brtm9/e8EaPsqLs0JxyQlsRKseWYz7Csox4VXPYsVdXUB8wgBilCMU7irpAJDVY6KCR0DGv/vpr/MsMdMD1kDeBOVEL/T74/ahOKrn9GLjcXAtvilH/bOVHSDCYya1d3p6fD9PNpsc3yKydtKRRBk8PbeWuF7rxwn/7+ApT3XPIfs+5h1gPVEUcQvofridAvZLJGkOnLk5P+IwsnQnFfCCn0OlYAoYo/5AsBfCKOwB3O8LQWU6pXM0FCRWPhkmakd8Kmz2L53MWG397g4k+V9I5+fX9kwZEX7Wo7JFDBMGSBT6zGzSlo5JhPZXhR1PGaAHw+lX5t/oH/3NAR7PsAvT9BVLd1GK+DdPc07E9Rx/qEzdBtd6bg/HyH3dXrofNqDmGq9RCpmdRVuEPa008leA62SXd2D1RMD/zC5DMHg9F4HA94NPkCz01WcSZuxoeYmPtFTxCUDJcIVEYfZ1AEAeXLCKRdGgYOjN9O/NbI5uDjckFJ0HZWAKGLPtAelLtfIKOh0Oj+gSBzumM5fA7qNvymaL8hvpj3AA1+V8dekn/fJ+6M2eYel//aUCKp4lK5ER1samZgfKdqCrosFdNIeTWTv832jA4OF+/N5F/d7i+XtE6G6v/uem/otmLQZczSNVKxqum4XltrC28QY8slMwQ26y/hS5kVC7TzfMwW/n54OPxpEiBPcPQ1Z1bSWofIZFUXcEcu2IuugvZLFMJOTWFHKtKgCgFLASmxUAqLoGZyZdPvF/8pEkXKeroKoocuIIfWtiBW2E7S06d3TvApQ724MXrvtYW8/f5xUQaSB6YFF3mFsP1wlFd5df2pn0WMG1g/FfIFfiwYfPCEgZ09ox3jM5AxXO1QV5IcTve/Hv58K7fO86y5WUe1Q0RYMam+6zCsj1uHwf8wK+dqxezxwhGmzk+E6/X164dXD7HzNqw0NsOfSJXJtWqZGS/5OL0WwnaDNPhV9npFWRgoaAMB2gv77X5bTlbh4ItTqqSKuUAmIomeYXIaQTPdNS3dEdRsdmCpCwg1yX4OYMGUuNtmvotemzuKvD9kMSYxpdLvh+NhtZGB6S3YrfZBmp+cRSld6g2qqzca3FqMKYKrZAdbUKnqRG+b2tVBSica9xXyiwbV5dUbLULrtCwAaqv6ZbUlqDMPboKFuPLu25/AP6YXXDwh2JiVnD6twJqp/DA0nv0ee/55rnsP936TTLWhT5uL4FBPtiaLoFaz6+lmx6utnQ/58x0gSkNR+1fTCpdHeuaqeRiUZvRaVgCh6jkuj6Rvp/8r0BiRUFeSuuxAuTAz6s4KB6Z+1GQxS1ZGY4chh5XWHJLno4dppD/LtPjHGLvGYaOSGKktXoslykZ5H8B/gptZi9LoHm81BDujtcIkhAEDmh/J1GaFWQXxtX9Rx90zxzgsRaw3jnw9ZgYsd1JUM0WPevwNc9Ra5NjzzDHSU3vVhHf6ytwqYfApGHnmMVoRj5uEKrVawMtUfReKz8KpnceFVz4b8AdvCMfkzOONmo4eF+Q4pEgOVgCh6juml/I+HbuNnQeKoBScu+PBRwQ0pW5PqyKHchvHPo8OV1PPqObc9jFwP8ulxO5DdrZ72IFJyrLFGSEwy65kE5IbP54gRFovcXIvzPPGYAX79WdRf9zGJ/Ob4/uf5A++Y7pVLfm8tXekwX6ArE74qSKhJiAxZsuYxs9UTEk4Aw3YCOAnefIuF7V/nWsQAAAZdHk4uHPoBHG9lrv/0w+Tmwisj1uErJweqWTlFANzv6A2fzxHctZia8xeArAPRPK2uoZKMXotKQBQ9Cp7+n3TQ8bAZAYA2IPzxUKWI5c/EnciZOLG72AAAkzYjZ6AXUwxO78wBgZiwG8lg1rfW0zBzGfjRw+L0uB3k+b1/UUDlmK3Y6HbTbXA+rxQqiPR5pUQ5CZG9t526Ek97EK2pVXRye28xLz7w46FScQrpMVSbhiyp8W1+dCUJ2fOS4PrkUzOPsoOwMgf24+cHs2vVlz1lKsdsRdBtQMowT5mLqcn17BoYnHQSqOiTNDEJiMz8z+7xQMcZNoUikqgERNGjiJkmdgdcisesekP9yDOb6XYNAKhhAmEACGuwNuKUrsSb0g1w67F59OcZz+o+U+aS7580OZrklZusK96JTS1Z9L+5/T6E+lJ6bXopO4AdCbIPPyo475Fbjs0TnSYgAJBkMEArF4BzAgMAwCYTMu4b7Q24ucpK8il67f78rl37zkzv50HAfn4AcLqNfxkj+58l/37D53PEJH+/kRvXY7rRyKp6cVU2uP0+RNtX7PMr+hZcRfPgNzlw8Bt6E0WcujeapxQAKZ3dBX705dMqBkhgVAKi6Hm4YcyZJhRc7+rd0+JmDiAeqLp2O3LD0InA/sKNyPYp38j7bCQ8FybSs04A3iRbtiZTnOoGsvagM04n7Lvs4C6jYfzz6GjNpnX6ZRWeH070JhPhbC5w1ZPOetk7q4LIvBBka0x17IbP5wiu8ljtdAa17lU7ncCJNeSZzXDD53PI89cQ40PtTtGzvH5AVJ2aTK/VlwK70RHDLoMcTYvZcyniB5WAKHqe6aX8UOgMA7JBmD1fzYL0MkiJUfB6bUj9NuKNXR76XP/PBRFwPd91lzeYp1qqfGIMFLK1blIzbgcGmCT6Uesi/Ck4ZPr9tz0sb7cKVW57eqk3caF+D+6Z4m1bo9S77s9HGPAuP3PTGZwgxpt7BJeAVDoccMZJv76aiwODVNVcJRWYavuadM6uunY7phmNcAtRPXSVVGCSwdAzJpaK+IKT8DY4+VlMznMmChwKsxL/yoh1vXdzqg+gEhBFfHDPFCTlOwEAnOn0LMjPbOG1bynikjfy1+P+wo3kDeXlq9fiy1evZW82bPtWT/B/LgiYYaDPlQqsbSfkylGySkeUqiAcTdftYj8DMtDl3LsBvEER1RrlC4hCdfa+6y5kH/P2+/i2tdsels+UcZ+NbA7Hng/wzRRyqaFuPCvdPCTtHHlM03W7MEvT4M7j80lpXgAgZ24axj+PruO/YE9T0QcwOPmEnhFQAAD40Ygvo3RCCoUXlYAo4gfmRo8y3X5rrRq2VMDeLrQFxQxZdeLq3cHrP5zo3aGn5iL+V6Z3jUrOfb4b8cCel4SLEjyYXOY1jqRaqu6exicavopGqElIZ8pXXKW1ZSi99rt3BZucmC/IXaS5GROu5QXkss5Wg4EdbH/fboeG4w/RB+q20BS/FFGHlOCOFtZado5uUMZp9jBHPMi0K3o1KgFRxA9MmVjMNPEB3Y3rESqfitYZKRSRhUsYbr+Pb0m6b7RcOSoegstpD/Lf0Xum8GudJQzhtGIB0ImLL+GhZk+mlyLk7AlWYxvwLrBu0OYLfPtKmJXZhs8fY9feHrkBP2ppIZNRR0mFt8JDXQv353uTWEZpThF7OIW8qNDvM9ZfqEYilPDGhfjwAFl2dqm6bnspKgFRxA8/nIhcW4MsCUG3m27RUih6AG7w96bKuSLbJlH0unkJsjNN1lp+MNtamxi+D1RS4Bs655KCcORkp5ci2E7Qx90zBTHnj/RxN65Hk6mDe73B6a3iEGQM+X/sKZiGv8yfH5ecvHpYdJZwNYx/HtmKxv353kSVqpb9cCIOSXKxA+uK3svYjCZ2bYiFz4OGW2MXHv750nPsdbl08LL4qW4rIopKQBTxhW7jlX8YpBUShSKGDPlkpjBJVKSKk5LkD8DtVN8xnZ93ml4aun8GR7QSGV+bGTcknn6Y9kS5expmpJ0M/fmstaxCVanNxkpWuy4WBA6kc8O7ANBwcRi7xvpOv7dWQL/P6DVrbdedp2XS1EwSk6Vp0CBrZ1XENT+vfias76ZNIptdaLWya+kS9/RIc0YibvHq+TUqae6lqAREEV/8eCiyPfTcMCuoJIRiLKGao4gup8ftQG5QO8dk6nxWxXyBl3bt9xnfRmO+QEvehkqkEhnusZkkKiPlPBvw6ELA8H/MCu21TS7D8SP+BIVHHw86bn/hRjx+toRWvupo4icRB8hOZ9Y+eEK0cf3z1lpgE5AB7/ItXf74Kh1v7qGNHpnZmUNFW/C8ritVrDiHq6C+kLc6rHucLAHJs1jYtVia1B5uaWHXzjLJyfzTi8WMk+ElZYr4QCUgisRh2oMIf3uK/8Ghbsh9mLgwGeyFcCZ9APLh0i4p3d+8xBtAUsnE5DI0mVrpAHLKXMT0f3TlGeTIZjm6y+33easgf1lOqjWlaxqZaDRdtwvzLJaQTccOFW3BZm7Hf9qDyLmb+6sGBbVkXWb8p7OFbPdY6Mn8Y0fCXfqeKd6ZD6otz9e6Rvwe1hXvRJfbCNLfUUXUIat9lxnJXFfXfxbeDI/sOs01mdi1vBgmIJWMUSIAwHtNdAvZGacTzuuqopfIqAREkVjcuB7ZH+/MDyOzC6xIeKJZ/eE8MgC8bS4Uw/8xS8jaDAKYOgu5FhtXSQVyzyEm7JYn6PFA6UqvgSih+HWkaAtyyjv7CsqxKw7sHTk9bgdS8rQA4E0GPiSSySlzve1ukt+SXLMZUpjzQY3fzZU62IdaffrhRD6RkUmvOnK8ql+KHqPpUh759we+KhNjmITh4OjNYSWusioHZzq66utnRXoMzQEP2Plrn1t7ZcQ6fE35gCQ0KgFRxCcHFrE3f84VGG5egmDPj9YZ9TqyDz8qqN3o3kA0qz9sBeSDJwRnqOUQQtpmEMTNS5BrxaiRKOiYLBdJP4i4g2nF0gDYdqszTidpuNcZU/v1gzmniN+TSZsxVfPQ8yATdyJ4zOBqGEs+5kctLe3+Gx0psFoBJjHBomx2I1w4iWOuQnLzEu8gu6SlVRE9rB8/IkypVeRartkc8danHEmV42grXeGrdDhC+73qJg0u/udatqZIbFQCoohPSlfyO5c3L+F/kXRb6N4BfZS64p1yA7Z4QbITLe1n/9/2iF8H2YcfFU1cW09jMXtclqZJTfwoHJSvRie4Sirwf6SkhHqYF6oacJmIqidNLkOu+nB63A7UhYBBRCvb3oJyrHG5yLkOGa+NWIcDmSCs6bpdaOREAyRD4TJHeJkIQYaJETL7+BGBBp09juWHExGST9HD/dNLvZU0qjV12oPeBEX9VsacNiHYuYwxSUmwYcgK8gL6z8b1YX1WsjarMYwoRrXTKfWkiTiytsRItCwq4hKVgCjilobxz/M/PJw05/RSZIc8FcFMmRv/P+5TZ7FeF6T5nT8RTkLqLuXwiYFkfuKMM3aGgdzOfGekanzAwQUj1o8fEbJ+dpbL1QeKzjwS3GEapJFVEABIQiQTrFTb1wDJp8ihYGG/mn0ernHllmPzBJcIZWuadKeaxFe9vGM6b0h59zR+TdYOpogaGiLb+pQjaXs60dYW1vNxbY0V36wUXLVFF4JtJ1UoIoVKQBRxDTvU6zGzu9+DBn4Ct4bRqqGIYxw5fDLB/f1ntsgnV7IWGsluMhd4xhNckpH69xniODMkmmexdCtQwY8eJt8zXQgy8K8csxXrdT1kVayyQUvxgN1Ots8dKtqCGmLQ3FCexQIwcSemGo3BQ8OMgWLq32ewsz45mgbjmR3nLE2TttaR+FeCOeECH2+9xQ+rJ4KHTC/CKqmQ/e/6enatXg+jQgYAJ5jvboPbzSY19boOar5CEW1UAqKIa9jd0ClzcRLTalIzbgfurevEb0GRWPzMxu/k/syG0kpHJKsgX8zl1656i/77bz4UlWO2xvXNfNAnMwWXSDiEgFRGIvfLtjaounZ7WK+t6bpdmG+xkJWJumJv4E9tQDSMfx6zNA3Ghyg0cHD0ZuSkfvcXbsSOSVb15eDM0TExkwT6LiGAa9Gr13V4hQnquFaYLnPbw97vBzXTddddfKJxzxTv2u/eVUlIjChOTmaFLLhqBQDAm42NYT3fe8wQ958uXYIPJMPfCkW0UQmIImHZX7gR7zw+n/7FvjSadgTuw3DmawmDxJOBrXb8zIZsYhAq760VrHTrrz8T8NXD9BpnIBgLON+QDtQ0DGF3WF1taWzApIfZDuXj+NhtWJxMS9bmmEysgo+GCOGo9Dg8HtZnAQDaB/hT/z5DuC6/tqbrdqFLiMD3kqmAONrSgPMAkbXQRMT0bcpcBPMFujIsa9Ny5PDeJIqI82D//uza1NRUdm0C8z2R8avza8Td6enk2nf69et+4qtQdAOVgCgSmrdHbqADz9vvQ0g/HNuTiXOOj90W17vwnXJvMYYls3z7fRF53YMyTnt3jCkyP/QaxHFr3eV374qwdqk5NaaOeMxeGV+Cwn68Rr9F0k7SVQ63tJAeH0eKtmCe2QwPfFUWtPb+qE1Y43KFXAXxVVSpVixHSQU2uBDwo4dFk9MakEg4SiquGBRKJG7R3Mi+jzWSwfXtdXWhvAyWVLNDajzHOtH3+0x5g8QIrnIPAFBq40VBZMdxPPv117CipoZc+7S1FTYNXUleq9UxnFlT9F1UAqLovUTTVK2vsdsZH8HJ1Fk99pk2SHqwTdnvs2vZtghIr94zBdnkJxJIxAjOc6/7w0eFw939nftNQ1ditdNJGq29fPVaTDEa4KGqBeQ8SGsYFRhHSQVyJmsmoxvSjUYwmVqDE4mWod4ZIIOTfb9k8zBcG9vwf8wSkTJ9a7puF9oMBnpG5vb7vEkUJb97WWmIm8lRRI4hDv5rzM1h/bWpXEwIIwGxGgysKpuspUs2p6JQRAqVgCh6N7s86oYaCR42J/4dqZuSozLPXVaN6y/LRd2lnO48bWxgPCFS/z5DBM1A+NY0D4QlHcvAtSGZANn2k2xNC0twoiQlmUxqXCUVaDUY6HPxyQeHahgI3veRa1eTqSKFw+lxO9AhBN2KNe1Br8kiVe24cT07I6OIHFUWXm2O8+V4s7ERfh/GDIjNYGCTYpvks25y9Av5uRSKUFEJiKL3o5KQyED5CSQS00vDj/J+eUK4Pp8f+nG6Te5bEy8wMw1NLVmsf0lT4zVsu1GovD9qEza63aSD/bZhq1BDhLU1y4LW9hduRAAAdhaMYdew1fhRSwvZilUzbgc2ud204eSkzQianZytsX78iGhkBtCtBgObZBgRI94eWa/r4GrNphcNTu/cB0GrxxOs+KWIKFZJixyXFNS6XHCUUbOS8WD//rCvoJy8tiZL2r3Q3BjycykUoaISEEXvZoYh/oO/ROHuaX33vRzwLj/jIRM7YAK9uIOZk7EmSWYTIjxcf3D0ZrQikq1YjwxYhF8yQ9x7C8qRC/xlHCnagoVWK7l2fUoKPaD7t6cEai3kDEibEGEN5XPSvN3B5ZtZoSodvoT43Y3kHIyj48C9IqJUMonEB/aNYgxzPU7t1y8sWdxHBixij+HOAwBAeJQHiCL6qARE0TdQVZDQ4Aaef/1Z33wfOQWhztYiNAAfMQ7QZnwcUjf2KBjZaYisFKnd7SaH1QG87VtU4tIZjW53UKtS4dHHxcHmZth3sTWoGmCyXAQNEUzG4OOEMx2ETreKaYiskljUHKdvXI/gMdOfuW+GhRB1cJVUIOj87riie3Bqc9VtbWyVozpME0IZT+dINkdahkb8+RSKjqgERNH7mWFAiGCvep+Ak+W8bzT2yd1RyfB7av7/oRd+82H8vU9Mq1VYTC6LeHL1/qhN6BKCdCZ/ZcQ61BBh2hdPBq29PXIDNrrdIUtNHynagrkmExQefbz9uFSDAcYmJQFM3IlZmhbQFpZuNIKrpALJmR+PmVUdkw31ftTSEsopRw6PmU+eS1diqMmqonN2nFvBCg5kaxpMZyRzZepYMqjvio8FZ8/yByZK5VaR0KgERNE36A1D1LFEEnCbTK2qRcMPdnD3hxPj75pLgHmUyjFbkZMBffnqtez5GxHBGMYwd9W123GUX+uL1WCAg6O9iYTNaASXX0WmY4tVgKeImVc74yR4Mw79gh3yjwill2VWqWRi6ixvmxbRigUA3gSFMjZUhE2u2Qx1TAXkqMMBFuMvIlol+4QZagcAqLqUHtZjKhSRQiUgCkUfh1TLkeAqqcCI7qQnOD5vCUXkqHW5WMPAsy4XubNbOWYrJiGGZbh5xuls9xvxDbYDeCskF93u9gqJfwLiKqnANk5tqgOcXHGDC6HBFeXLp3QlssmEr4pF+evcvMR73HtrVRISIV46fx5+y6hZca1ZAF6vnHCQJrdcm92el4T6fVfEApWAKBISWWlZERrsDv4HTwi2DYPrLe+NqAAs5ogJu9ENAPBhsBLVkaIt2CYE3ELI7x4q2oI5JlPI0rwHR2/GHJOJXKu5nGDe8Pkc0VERTEzYjVZEMH38iDAZmUH4D55gA7pUzQPscZHEl0xQqGAzZnzU3MwOk9fpvFHlAXvo0s8AIDel5NqsbCcAsg6E9XwKRSioBESRkOy55jm16xwhuOFYmLQZ2baS0pU4PqMpimcVR6iBzB6B9VYBrwM7pziVbjTCCKsl5Od759IltnqShAgn2tpIgz/XZfUr7nxMplZAjd7BthoM0tcZcah2q6mzvMnJH3fTFRLdRldIFCEj83v5z8aL7FqtpDoi43Qb87F9+KjgftsfGuyGm/rRalwKRSRRCYhCoeCZuBNJKU/w3ky5NplewzvbRTjGc4rIkKp5YNAnM4OusT3XPId1uh4wPO7j7ZEb8KzTFbI3yKGiLehgfDAOFW3BJrcbqhzBLS2OkgoUejKrgGUEiJhfSrfwtVtRSchtD3uTEMqQ0jcPppKQblN14h52TdbKeaRoS3jXD6NUl2ESMKj/l+TahOTkgDZEhSJaqAREoVBIzcdGpraSBnEHR29GUwQdnOMWJgGhdsN7Auqz6S00XbcLG3SdFD2oHLMV2zweGEIkKG+P3IC1zOB3ZziEgIxDvwh6zEEmEwyy0Ne7rMrRJvEG6c+4VEcNn/wuxbQH+8CXuWfJGP4av8hJn3cHpno7tV8/1ogwXMUthSJUVAKiUChYt2sAr1cCN8xoNRjCGvpNGDQ7G7RxrsUAQAbF0SLs3dEEwdGWxg7MOoSAM0yiYTUYyAqJjByTCWwGA+QSUqk5JhMUWq0wnkj4rIje9hpiZoU0NLxMjyXwnPIVAMB//ZqvglBtWoouwxlfAgBkX/06vfDmHgFv7gn9fX/rLcElIEdaW+EE4y0y6/TpkJ9KoQgHlYAoFIp2qF39uuKdeHzsNqR22uuKd2JUZUR7mIy0k+xab3jdCdFCd+N6NCXVkYluzbgdqCGS1+3+wo140e0OaSDdZjRCw/jn0YoIN1XODTiu1uWCfQXleMblCqg63VQ5VxQnJ4OrpAKtIQyUD/lkprCH4eDebabMRbDW0iIS0x70SvNSicbUWd55kN+/Hv/XTJzCCR0ASJKTrAO8L5MMj5kVGKg8Naldaroj4Q68KxShohIQRcLz/RP0jIIidKqu3R5yH3JvlqFtGP88+drwo4el11yivCdSp/M4wlVSgboQZIXBVVKBnE9CzbgdyDmrU+wrKEcAb3thisEQkLz4gse6Yq9BoQ9diPZk1FFSgQHXxt+eEkea6Lf4dHMKW72JOjeuR2gZSs91yFqxPGaA5FPRPLNezeeM0zn85kOx/x/30mtf3yVvneMwOAHumUIfJ3G6F99MDvmpFIpwUAmIIuHh1GcA5LMN8O+nVOKiCIvipCQ4PnYbeXMP1VdF0TWqHB7gKgy6EOxMTp2uh9Um6BYC/JOX90dtav+8c00m+NGXTwsAbyXMX8q6wGptr5AU9nPwbuMGZ2wVsDqi23gpVs6g8I7piP0q1TUeJpWf3R/6QeGKYNx1F39tydbs+eE9n0IRIioBUSQ8b4/cgNwAn2y2IayytqLX8vPqZ7ocVMmciaXa+/GKzPMlXpjolYumNhUaxj+P6UYj3PD5HHJYPZxZpb0F5XiwuRkoFbiXr16LbzQ2An70sDjc2gof+RnFVY7ZimecToAPHxXnXK728+7IoKQeqn74uP0+73lRLVW+mQ/CA0dM2I16Fw0YFVdI/fsMAZkfkmtDCv4NRo56mVyTDq7LeP1AeJ8PJ72uUESYBLxTKhTBDBnx29APuntaQrSfKCLP9Z8Fqyq9kLcasw9fafGRqVxx1Q/fPEDCMWkzQunKuP8+OEoq0GowkCpVh4q2BLRG+VOv61KnadnzgT2fbFVylFSgsF8NwjEgqFWvwYWQqnnAKklGcyXzADHjjul8EjJlLoIjhxxKFxN2o8vFD9crgsnSNBg58Bi5lq5p7G8KaxTbGdNL+e+zbKhdbcwpYoRKQBS9gtPjdpBD0p3y1lsCfv2Z2snrY3ADmHWNQ9sHs6u+KaQP/sty9nrRENvnCBTRweHxsG2XDo+HTC5rxu3Ahub+0s+Omi8BAK9HBtdG5TPq68jEndjktEJNzfXs0x2snsiuxRTJsLJ0DYCskChoJqWkwARmc0KmjpVnCd1UEwDIyl07yttIEQeoBETRa8gP54f6rrvQVLAl8iej6HFWff2smHOKbiuaf3oxfXOeXIaOkgqEA4tEextKR25ewiYYKvmIPk3X7cImp5UMfvcWlOPBCzY60ShdiWC+wEvQduZ4z6k/XRrt3cjoyKTN3kHv33wYvPbmHsGZxMUc3zwA1bJz+31eg0Lu9TEVEkUwVoMBOEGE/hpf5cgjJKG7xI3rI2peGGseO7lQXVe9HJWAKHoNb+Svx2lfPBnyj1ae2dy7vSz6KAuveha5FpgNQ1Ygl5wAAIzNaOIfOBxN/p6gN8ulTqIrWADgTTS44Wp7Pj94zfG7dwVcGk1XAn55QoBuo9dePexNMmQVhHjhrru8VZ5XDwdfM3fd5U1CqARl2oMIzszw5w36EHsuXYI38umk4D8bL7LHyebNpMhmuiQqWNK1GLJt2Cq1mdPLUQmIoldR7WRaJQDgFsYP4PjYbcj13yoSm1qXCx6qWkB+7puG8jMPZ5xO3kwwUWaHfP39DAmfdFtr+d335FO0s/TUWQiaHYLaLv/9lIDaafTz2POv/B+lnNcy1LvW8fmstd7zSD4VnAx6zPF3HcmSJWcmvzPuyImbXfN4pubYT9i102e/xa69dl6yGSLjmyn8Gvc5/+5dEXKCrlAoFAqAaV88KTg1oyGfzBQJH3QpIorMiG9QDN3MFWHy76cEK6f976cE2f7kW/vliStrrx4W5O4/QGCS85flIqD1y1897MAiETBH4j+4/sfdIqD1K15nJ379GT8T9+ph+Rr3/im87HmJf3+46xQgttWl1w8IVc1SxApVAVH0KvZc8xzWMuZep8ftQJkTLSXhqejduIE3FRxjtbJVM0Wc8OOhCEN/Ta/5qg8UBicEHJf3EsC9xXRFwn9g9+YlGLB77MiBdvWw0pWBA+m2E1f+23wh8FzidQj4vtHeViwqIL632LtGVZZ8750skO7ryD7zAe/Sf6dmb7pKOLM500tRqp6lUEQQlYAoeh05JhM7ZJxrMgW4G/tjMxjgga/K1A20N8LslLtKKlA0XksOJu8tKMd958OUwFTEDFNqFT27ML3U225FVRvuz/cmEr7qhazvPf1w4POlHWuvnJlSqwL/rcF5xR/DXzlrchmaTK3tyS5av+n8hfUUvkSDwnyBD6QNTm/bmYJGJnLAtUT1+ww475BO8fm8hMIfdyfG/S/ePYsUXUIlIIpexwt5q5HTTn9lxDrk3AD2FpRjbriKI4r45sdD+aAKIDAA+D8Xrtzcvpgb+L8VcYerpMI7dP5SLTkkHZQk+Jhchu2zC7LgsLE46PkcTcMB/rhbuFqzA//tpM3oah7sDZA6zEW4SirQZjBA9uFHRdjeDrHCY6arGfdM8X6PqJ35H070qowlShCbCMRawOC2h2Nb/SD8dbpEnAzKK7qHSkAUvRKHEGwVZLLNBuW1vB/AgjNL1A20N+Ix08nE1FkIX99Frz2YE5i4JHKLCTcrASBv9UhwmVXXyf+P/9xahsoHb7m1yWXepKdDcgIA3gqBZierHBoiOMJVNYolP5zorR5R18Ud03nlq2kPegNY2bxDX4WrHL31lgB7Pr3WMtRrCKkIRLaZpEgYVAKi6JWsGLwMNwxZQf5wLx28DF2MHvua3OWYFu+7kz3E909IjK0SgR8PRch9nV8D8A4ZX7MxcO3BHIQPnhBDPpkp4IcTEzcYuOotfnc680NI/fsMei0RnJEfzPF+LlQV5J4p3nYr/6FzH3dM965xKk66jZft5YKgG9cjtAwF0Xht0FLD+OexydEPGpr708fGE/dMCfBNSV18+sr7N72Uryh6zGqHmiKcakYiSDh3hzB/W1JtX0f4RBQ9gUpAFH2SDKMRXj2/hgy4zrlcaiCd4I389chJ2t55fL648/j8uH/Prs/9DNjzvOotb497KSHPq9vgdFMG+7gJMTs0dRYGDEb7U7oSm1qy6DXNzg7qxxW+ahWVaNxbjGww58iRe4NwSadPfpaqBPgej/JiaRnauelhlGGTzY6YL7T/Z9OKIcHvA1Uh8XmDUMPqfZV3tgt2Psbg5JPZXr7Tf0taErsm82lqum5X4m4EKdpRCYiiT/LIgEV4zOEg1zYNXamqICHy9sgNOCklpadPo1MOjt6MWZoGM04SUs3THsRCG9MeU7rS26bFELZZWKzR7FeGpDuQmlwPGYd+QTpe55nNiSNLLNs1lrWhUYmLTLlIs1+pElDnMHUWnfT4/DZ6sE2pywGcr/2H6tW/ZwpC8im6qnbPFG8LV282wwwFZ6a8KtSJZ09vh/tC9wAApUhJREFUpV7nJjIBPmppieGZKHoClYAoej3cLEiN7oJlZ5eSa3kWMyxm1voyxcnJ7Hu259IlKDz6eNy/ZzaDAZrddMIw0mKhVdJ8cxDELMENn88RnMNx3DFxJ6YbjaT/SdN1u9DGOMdXXbs9MV7fj4fyg9C+NruOSYjHfEUVq+OarEXEp45FKW35Epe77qLfN9+sRCIwZa73vSGU4mDqLL4V66676OSsL6Lb+DY/WZLbyysgsgRExrQvnoz7+4yic1QCouj1nGHc0XcNW41Zmkau5ZktkMOs9WVmD1yM3M7U/sKNWDlma9wHVtuGrcJal4ucaTlgt8PekwXBO7fWWm8wQLQwvf/pD6J3slGgrsUGjubB9Jque831CKyIMPbT2fF/47+3mA98fzwUg2RN/VtjOgZ8nDv6O9tFe3Xj9vsQJpcFXvf+z+GfDP3+9SvHJVISotnJax8AAJJPsVU1sNYqyVQfXDIqSzJ6eQJnRf4rIFOKy7P08tmYPoJKQBS9HqvBwA5QH3G0AjXXUDZoKVY7nXBT5Vx18+zA2yM3JE7gxLC3oBw73vxuOTZP5Fss3p3KDkHBtNyvvYO3t9+HHeeDRhb83+ifcCS50VutoYJGR0kFQstQcre76trteKRuUPjSmbHEkUPv2AN4Kxd/8/s98JnA/XgoQr/PrrRG/devBbs7XTsNuJa87MOPigyT31P7S5t6zIk5WDxlLmabgW7Ru3E9GoG+nuDmJd7X/rcEF7DoLrJEQg3sk1iZauwDX5UJroKtSCxUAqLo9bw4fA3a3W5ybdew1VjJzIJwKlqKbhBHu6EaIjx2cmH7+aQbjXBw9GaEO6Zjx8DT5JesFFqt7TMkN3w+R+SYTLE65chx8xK2+gcAfFsI10YSb0x7EMF2gmw1g9KViOZGb1B8YJEICA7vusvbUvSXyzLdYaj01NkzgWtlA4+Z3wmPc+qKd6LGVMEcfh4nQQeWrpR78PQFZAaNshasXo7OqFECADQy7VkOIaTHKRIHlYAo+gQ6AFz/Gd1acqilhd0ttbvdAB8SN1WFHG73uXQlkkFKD/Dy1WvR5lfmDwjIb3sYfa1IhUcfF/69yhYDggm8MWSOySRtFYhncjSNbqmaMhcx+QzdinXbwwjJp+id8Hhj4k5MZT4b4RjA7zwnn7qiTBSOm3T1g3B63A5eOSvR8LsO8iRGrVaDgf0uoNaSGNdMlGDNMAG81xvFB0/wFbheAvs9AW+Vmvp7o67DKyPWJWQSrwhEJSCKPsG+gnI8WE/v3jpKKpC7CRxqSIVBFvVbR/LOdsElGkP6f8ke1j+OZmvsbjfMOPmM+P6Jp0THgcjr+3krHO4Ou227hq1GFwiYc2qR0BATtiXtUNEWtBkMcAsxdC8m7OZbZ25cL6+exBlk4Fu6EsF8ATJSzre3pPmvmZLqIHXAB/yDZh3wDq6HSiK2X03a3P46D47ejMcdDhhPJK4143ZgjslEijiICbsx3WhMjBmiKCDbpLglu5X8e4alDVL7VUfpjBjiaLONk/7Os1hifSqKKKESEEXfwZnJtwDZ8wHe3EMGKoVWK/zzl0/HzQ9z3DB1FhuAyXa24mlQfduwVXhRd0O60QgdlawOjt6MNoMBckwmeH/UpoC1XcNWY6PbDf21xKx++MOJNIDHzA7IOjyehPDKqSveiVaDgZYQnuT9fIf/Y1bQmqukAqWVrY6D7P70clUjR0kFpmsamYTsL9yIRmawuOra7TjYZOqTv6VaGMPWY5KSoDg5OVqnRDNxZ9z8NhdYreTfX8hbHTfnqOgeKgFR9B2mzsLC9EvsGnfYvoJyfO3kwGidVUKD/Srpvu8EYnpGBkxOTSXXrAYDcLv9Do8Hzuv0bFG84T/r4s/7ozahjRFpuKW/G7KtTlJa+fS4Haxkb7xRM24HcnKfGiIrBZquabSsdEfJ3Y5wLTW/e5c3o0sw9hWUs1WwNiFYmdRUoxESp3YWfW45Nk80MvOJtS4X7C/c2CeDbZmcu5Lg7T0kxh1EoYgQlY395AZgnHvvpdG043EfR0zYjXXF4e2aUTuoPcEPMsvwRFsb6fuSb7FAlqbBz6uDjQs1RNCFYH1R4gkX0Kc47YsnxaGiLXiirS1ozWYwQIlkBzbPYkmY3WxX0/Arg+V+VNmTvSaSHeZdrB8/Ihp1Hc65XEGD7NnppyCb66R69TD/fqQflg8jJxj1uk5uPuwrKEe3EGRS+9qIdWgzGuFHCXLdRJt9DW7Y10AnIMebE7+6Gi6VY7ayku57rnmuTyZlvRGVgCj6FqUraXdiAIC7p/FrSiox4tTqOow88liPByIzTj4jdCFI1RUNEU60tUGtyxW0lmI0wJikJLDEeSVg2hdPinMuepd/pMUC3z/xlMjStKCgsNHthj3XPIfndZ3s638hbzWyg+zxxuQyBN0W2IK55yUBLUO98y6XRgf8c0dJBZ4etwMbxj+PjrobAtbqWmxQ55DMcrQMpf9uO5FY3h+dcKhoCxYybTJ7C8pRQ2Svm1pdZ0VBehsZh34hHB5GNjZRZZkViggQ33dOhSIatAyVVzOotemlCNNLe03wEFMYD4CacTvweFNSrM+G5ERbG5xxueCBr8oCzjXXbIZ9BeVYr+tBFZsMowYrBi9DEyKU1wbvrscTnGyl7fLsy76CcswxmQKqOb7g0lfhomY+tg1bhQnTUmOtBbR+E/C/21svrbW8V4VuC2y7ujCR9QCB3Ne9Rojc4/Qy9hduRHK+BrzVDtmxtgRVjwuVLE1j2zgBIFgEQSElUaquis5RCYii7zG9FDHv39k16bGqDSt0nJl833wc3HxTDUZ4bcQ6fCPfq+604MyS9nM9YPcOFL8/ahN2NMbyzUDMy1kSZGoYT7QJwSp1+bdepRuNAcOy/jMeNqMRJjDtWFmaRu50xx2X1buG+AJmf/fym5d4vUEo6eE7pnud1X0KQemHAe6ZQn/gnHM6AHSssvQWsjSNdULfW1BOym7vKyjHRiKp743UulzeNj8Ck+VijM8mfpBVwP586Tl2TdYWqkgsVAKi6JOICbsR/rg7tCpIV9b6OKTk6eQyHJtdwx9EBX0xpGMbiS+ZWPX1s2KM35rVYGgPtB/4qkz4t2XV6zrMP704Lq+LHMnu6wk/E86lg5eh3eOBB74qEw9VLRCt4krQ9Eb+eswxmWDV188Gvca9BeVoMxrhoaoFcfn6/fGpYo39dLbouHMvJuxG1FroA29cj+3u5jKVK24A/d2NglTAIuZSEo0jRVu8XxiJySiVhBwq2oK1uk6qkPUmHEJADaMKKK2M9PLWrIOjN7O7NtWcMh/IvWgUiYVKQBR9l/TD9N+nlyrn3jBpOF9E/v1I0RbWgHB8quixndCKb1YGPO+moSuxXtfh59XPiNMuJ/gr1Oy7HGjfeXy+SDEaYNuwVe030KWDl2GTJz4VsbhWl3/+8mnRUVN/Te5yPONyQb2uQ5sn8CNZeNWz6GBaubI0LaECA7vbTQaFeWYzO5fU0JrqTZZlgaG/q3rHv9tOBP/95iXYG5IQV0mFt0pEtLHVFe/EJkbpqWbcDuztrtayRjPZa0dzY8TPJZ6QyXj//iJfGfqrvXebM/YlVAKi6LtM3Imc2RFYa2m1LF9ywqll9XFSM4+yEoqcWtahoi14yLeL2gN0bI+wezyQpWkwxGSGTUNXBpyXLgRw8rNjrUnw6vk1Ubku/NvCQmHOqUUix2Qi1+5OTw/yPgEA0MA7fP/i8DVBa7UuFztYbPd4SLWweOP42G2YbjTCTZVzg1Wxrt2OjW43PVh/43oERw4/y3FgkeAqIENSG3gPkJuXIGfomVB0cHn3v2ZNiKzJ3elWU49XQaNJqmTWRZaACE+Mp6ti/Bl09Fbyp5pQ5fOxt6kpKuejiD0qAVH0acSFCfTC7fchq2ajYGm6bhemhqkKRTlyx4LZAxcH3Ah9gTcVHLw9cgNmaRokYfBrnDlwMaYajfCvdasi/jrW5C4PK0ErtFphxeBl5LHT+vUjj9lbUI4TkpODqkMA3kTjtMtFVqw2DFmBnKdBvHGoaAt7rnXFO9HFBYbJp/gqh27zzjsRZGkawKTNyF7jU+byn++BRaKjFHBccvMSTLVeak80/K/Zput2odXoZg0hUWuJKxfuSCJLMrjZkNS/zxBooJXrosYkviUq1sjas+5MS4vlqSiiiEpAFH2blqG0A7oPqtLhG0BVVRASWf+ubKd3X0F5zG+AZwh5XQCAQy0tUKPTaz7/D4rqtjaIVhD+h4sbQr7eKv1mPDpCSQsDAPzq/BqRZ7Gw7s1DTCbIZdqtDtjt9BxQHFLd1sZKCFd+kxfk/wEAV5IMojqKtq8CB9sJLIikP4aU0pVoimORA3+artvllTt+662g1+goqcCaNvqliwm7sWMFpbfQyknwAoDLxasAJorRZ6zp7S17fQl1hSv6NjLvD07pRiFFakwo2+kFuKJQFAMeqlogzjDJklsIOOfSScO0LE2DQqsVdpxbEbQ2c+BirHW5WOfx7vDdtPkhX4/pTPvHjnMrxAGml7pR16FR16Ha6SRbqvYWlONHLS3k8HDNZYf0RBgsbrpuF+aZzfSu/JS5OMRsppOpm5d4fzM6BNnSgeLL7LnmOaTa3rpyrqEe02PUl/LzdS1DAf7r1/S10TK0V7ZisdsRf1kuZBWzviJTHCr1hF+TIjFRCYhCYXCGXwVRRJSuBHGRotrpBM4gzGowwBv561FDDArCdSFgZoe2LX/ipRWpvHa5kAUx3Nrh1laYOXAxrhi8DA+3tJC6+zXjduCklBTy+NPjdiCX+MQbb4/cwPqYkIGOry3TYw5qxaproWdDsg8/KvxbbWSCC+M/nS0S3qDvrrtwUNo5uro0uQwh/TCdoE57EK2mVmmVNBFxuenvAiafAVPKWXIt3WhklbP6Oi9fvVa9L70ElYAoFHdP43/QZInGPVNQmrgoWFL/PoN83zREqTpKJLEgwiudmKXZPZ6gJMXnfN7k8bCVDrvH0+OGWZUOB3AVngN2O/xL/wXka7f7JU+FVmuAVK8/eRYLOxyfazZHpQoUDXJMJjIpaBj/POZ2rIL4BtDvmI6Q+WH7dZxx6BfCyng61DkBjjdfCUJlgguHiragrP89UagZtwOPHHkI4NXD5DA/p6bmKKnAQf2/jPbpxY4PHxUyA0rVTqToy6gERKHw8fvX+buBLNFQSUjIcEO+B0dvRpk6SiT5klFaeahqgfBVYt7IX49FSUntTudbzq0QviH7skFL0WowkN4Yb+Svx8YeaBWYcfJKteZEW1uQipcPLgAECKyMvHz1Wsy3WgMe18ea3OVYr+tkovH2yA2Yb7WSg+zxxsHRm/HQuWEAf9wtOs5nHCnagg114wH+slyYPn5EBEjpTtyJvvcqS9Ogo6Sxj2wzxNWAbzxQc/K77O+mLkTvasWS+MaICbvpTQDJ3EhvgduEApBXCanfIkViohIQhQLAWwXhbhSq3SpsuCqAo6RC+p5G21n7ga/KBNdCBBAYAJQNWtreUtWo6wHtV+lGIzt0nmexkDMk0WTXsNXt5yYbYu1ovujjoaoFwtjhk3ltxDpMNdBtJEYE9vVbEdlB9rjDmQnQ7zNSlhgAAHSbt5WmQ9uVzWCAQZ/MFHaPByrHbCWP7R/DtsK44t5i7/tBVUHuusu7RiQhdcU7ES5MJAfZexNC5x29jYnyvekGrNIcyKuEit6DSkAUCh8ec+hVEF/7lqqCkLw2Yh3Kdro4xaS9BeUYzYH0FKOBHdDOt1jgNaI1a8u5FcLSIahfeNWzWOtykYnWC3mrMd9q7TGHdC7433FuBesNkmc2kxLDeWYzWc3YNWw1Zmlae4XIn0cGLMJcsxn+2lQe/9+NaQ96B8vfWxt8rrc9jGCtBZPRDSZjYLJ1fOw2BPAmWxzXJ/OBZq/n3mKvb9LrB+jfTmcmwG8+DF67/T7vcYmehOg2gIm0KMfwZL5Cer4PDFoPYn6DAABYfy4I3GRRJDYqAVEofNx+H//DJpsTAeBNxhRSBZ+GNrptBQCgNoo34f+2Nwc4mftztLU16G8rBi/DWpcLLhK7/S9fvRY5ZZZal4sddI8mP69+RnAJls1oZIf98ywWsm1r5sDFaDUYSCngTUNXYr7FAv/ZuD5o7btp85F6P+OSG9ejNbWKXRthsUCpLbifv2bcDuQSOoC+0U4j5YcTeaVB2e+mbgOp63wiIJn/yGda9voKHTdzugJ+9LCQJSeKxEIlIAqFP4S8ZjsGJ8Afd/NVkETfresJJL3xRgDaiyECcP4fI488Jg4xAfMZpxNOMHMjNS4XUOpFL+StxkqHI+atWIVWK+QxXh3pRiM7gC5zIM63WNi2LrvHwx6bpWnwqyg5xEeaUVYra4h5k83GBo21Lhep+nTLsXlCVh3pM3jMdKVjeilC8inSV6W9LZZaSxQk8x8yvyRZQttb4FoWAfjZGOFMB+FMj9o5KWKLSkAUCn98vcncGrdj5zGrKoiEtTXL2CBi5JHHROHRx0njss5mRcJh5JHHhGw2gapyAHgrI5UOB4w88ljQuVaO2YrcY+4tKGdVf6JFlqbBUsYBnUuiAOTD6d9KnYdHW1tJ5at/6b8Az7hc5IDoDzLLMEvTyApJvHGoaAvuu9gKJiLxNaI3MHzgq7KgtXSjEQqJYX0NlG8BAFyZB6Ekze+Y7v1dpeR375juTUISVZrXJ9tMcLyJNyGUbQT0dlL/PkOwG08eM8CNofvoKOITlYAoFBRcNSP5lNdAqiN3T/Pu5B1YlJg3yihTNmgpTvviSfK9sRoMkMrsrEdjGD1L09id/DFJSaz+vs1oDJCopR73oaoF5Pk2ut2hO2B3AwuTDC07u1RwDuhdmVV5ZMAitt1sTe5yzNbonVsrInDGh/FCu5rXxJ1YnJQUUAkpPPq4+MPFS7Bi8DI83KFC9v0TT4lDRVsw12QCEwS+74VWK+wtKFcBE4C3FQuAnge57WH+PdLsANbaqJ1WNBmS/Tm7huZGeuHAItHUkhWdE0oAmq7bxW882fNjfDaKaKISEIWiI7IqyI3rERqL6bXkU4nfsxxF9lzzHPm+HrmseEK1MO0tKEdqN7o7ZGla+/BwR1ol/fr5FgtUXbsdc0wmcuj87ZEbMM9shjmngpPQfQXlSM0PRINfnV8juCqODt6Ej+JEWxusyV3eabB82ulkk6lalwvuPD4/aO3b/Z7EBrfO+obEA/4zQRoiTPAbHq8csxWrrt2OAF7lK/9k+qPmZgDwzsK8Z7cHVMgSRgUsVmh2vi1Jt9EiIJPLMNV6iRWsiGdk8z+5XJuVpG2rz6Pur70KlYAoFBQGJ997bK2lKx2TNiPoNlUFCYODozcj1xrkKqlA+PDRiL2nnPzjjJPPiAxmcHvGyWeExeANJvcXbkTOQOyMy8XK0g42mWDHuRVRvzZqXS62lSrfYoEVTGuWrLrjz55rnkNdCHKu5YW81TjYbCK9QXYNW409MZAfDu+P2oT1ut7+Gv0TjvdHbUITItxUOVcAAExOTW0/7kjRFixOTm6vhL3X1BTjM49z7rrL21LlUw38zYeifTbktocRzBfIdqum63ahLgTZGhfPNFwcRv5dNts2JMkFQ1IbonZOCU0HGWxFYqMSEIWC4o7pCOmH6bXbHsaMlPPkEvarBLR+E73z6gVkH6aTiUa3G+BvTJsSI2UZKjdVzhVnmRakcy4dZN36/vKPA00aGWS/kLcauXare/svwEKrFf6jYV1Ug6h6XYfZfl4l/tQwrx0AID0Ev4p0ozHAsNAfSsbX/zjKuDEeeXH4Gsy9PMjfMTF9e+QGLE7y9vC/fPXagPe6v2ZsHyKOVdUrobhjuld+9/UDAn44EdtbswCutGK9s51MQlxNw2N3npHg0mjyz45z34bTZ79Frp2+OBBOM22gndFTkt8xo99nPX0GigiiEhCFggG1FuB23jm3XjFhNwqPBsP/Mat33wi6QZ2DLqOLCbtRJlsZCXJMJvAFjh2xu92k/wcAwHk9sDqwa9hqdAhBKia9kb8eOQnc76TNx8MtLSGfdyickajrnGXWpn3xpDjhcHT5OV6+ei3WulykY/GmoSux2ukk29SWDl6G9brOzsrEGzaDAcprlwv/KocPDRFWff2s6CiwsGvYatQQ4efVzwhO6lkhQbd5jSEpNDs9gxeP/HG3kLZTcb91ksH1ztgwZEXvvt4itBGliA9UAqJQMIgJu5G7GTRdt4tdG2JBqZpQn0civcuawcFlc6puBh/vNTUF7Vj7kMliUoPXtS4X5DBVg3pdZwfoD7e2slKvkaCSSSSGfDJT7GVagoaYzVAcomHe2yM3IDdP8vbIDeywuqxNLd5YOngZuoQge/k3DV2JjW43nCOqSisuJ1qxll9OGHxVD1/7lb9E720PeysklBBI6UpeMSvOsPb/GEwD/0wvZh3gZw255EsRNVl2Rc+gEhCFQoZuYwNiACBvhKfH7cA8sxk41SeFNxgmFyaXSbXhM1JruvW8nL7+/NOLxQQmAL/z+HxBqWa9PXIDHnU4YBDxWnzBObXT/0b+ejY47y7zTy8md+sBAFKMRtZhuV7X4cXha0LeXdQQSVlaAG9FiRIWeG3EOmz1eNpnKOKdURJPlRyTCXKZtUKrFfL6uNmclOml3uvt9697W7H8kxCfSzol21t62Sjzv34d19dPksHAqu1J6eND6DKxAZMSdehVqAREoZAxdRaaOHfkKXPZX8P3R21CLthTeJM0yvujHWaQP99iYWdIOuNHXz4txlit7DoXLNbrOrw9cgP5WdfrOut0PthsYj0gXELADZ/PiXgA9dL58/DOpUvk2o8yMqCumG5hCCtQAu9AforRAIvPLg16LQdHb0YH8zr3XPMcJpJBX47JxPqYODweeJUwWtQQQRciYdrNeoTkU1f+238WBMArBMK1KV0aDeDIieKJdZ+Gi8OgYfzz5EWOBsm9wXYiWqekUMQVKgFRKDrB5Urih6OTT0Hq32eQa5UOB3+cAirrr6JbKSRVkIOjN2NdmH6P9boOnMv5Ry0trAQt55sBAO2eIVQVxDcLQLXhVI7ZirUuV8RnhYqTkuDQZVnjjsjanlKM4d8Kdg1bjY1MopUj8Vyp13VyhiTeGGgywbdS52FxUhKcbNsWcL5ZmgYLr3oWz7pcQWabGnh78vuCq3XY3DEdod9n9O/APVN435C7pyF4zPRaAiB0SbtjuEpPVLUoAZH9TnEKhorERCUgCkVnTNqMrGnUpM3I/Sg2XbeLP04hd493ZvJ93hN3orQtjqHa6YTKMVvJ4Fw2GN5ZANkw/nnk9KNk7VY2ozHisxBGSVWBm9d47ORC4e7mfT1d06C8Nng+Z29BOeaazeRA+iGJ/0s0CFd9y1fJHGx5DF9vCJRHPeqX0DZ3qITV6N7E1WYwJIzyV49w8xKv/C6hfCU1L5xeinDVW/G7ySMzT+R+9wDAZKI3STrFl7AlAOHKKWeEoNSniH9UAqJQdIH7MzNZ87XxycnAtRNNTU1Vg6gcU+Z63eOpAGLKXATzBba6FA6cKZj140cEtzb209mioQtJgg58IF2r6zCW2Ok/UrQFdSHCbinryANflQmuR3ptzTJyjgXA23rW3V36FYOX4fE2Bzn39OLwNWyF5FDRFnaQPdIsvOrZsAI0n9EggLca5i+/7KuOzctZgg6Pp92E8oGvykSbR7Q/72mXs/dLpHaH0pUonX3gAnaDk3cU72lkpnmc0tUfdwtX8+Dwnu937wq2ChJn1RFaI9CL8PBJhqwarUg8VAKiUHSBl69ey5rPvT9qE45k5gf2FpSrFgwZE3diRhKtzJRtk7QiTC7DUAwfU/8+Q3CfX4amsTfEI80utmriT824HXi4tZWUbT5StAWPMq1fTdftYiV7Q8UhBNvu9Fe7nQ3A7W43a04YCl862tgh0RyTCX5e/Qz5AVgR41awYf7pxcJf0W7DkBU42GyGHedWiMVnl4osvx3ZDUNW4Im2Nrj12DyhIQa4oPt7yCgkUFWQ6aVe5SufeaE/kzajBdGrkBdv1Jfya7LKb2NxeM+XdQAgZw+9FmfVEYeb/83LluRtwjEgCmej6ClUAqJQdBFuEBnA22bBBVH1ug53Hp8ffzfIOIEb1Kwr3ol2j4etLoFmlyqm+NPk6AdcoF/TlAGOkgryHIZbu/4T6XIbwWqkqyUaIlvNyTGZ2OpaKKQbjfAK42OiSVqzPoqQL8negnLMM5thwZklQa/FJ31MtWLtLSjHZo8nLpOQHJMpqDpkQoQTbW1Q63IFmTE2ut3gEAIa3W54IS8w6ajX9bh8jXGDb/aLUre6e5p3jUhCHCUVWJyUFH/zRFwF5PUDgh2g123yyomMm5cg3Lie/KJLW54oueNoI5EaZhUC31srwn5vFHGJSkAUihDg2mxeGbGOVb16+eq17JyIwgsnySom7MY2pj1qUJKLTSqC0G1wfOw2+uacVMceFpKM6sSdmK1pZMLkKqnALE0jr5/9hRvRHoFZENljnGhrI/++4MwSkR9BqViZ8V69rgOnfJVuNEJqhCpBkeRwSwt0rF7Ny1mCdo+HrBztL9yIuhBkEPXi8DWYYTSy/jAKAJg6C1mzQV8SwhCrVr4uw7WUWWt5BS+Ds9PXGQ4uF22+CgA9I/srSUDYFizdxquiKRKSOPvGKhTxTaPbzfbsHxzNG+ydZVyjFVfgqkTpRiMp5epToOrSrjJ3kz2wSLhas8mlW47NE6GOPJ4etwOHMC13OSYTjGFc2HUAck4kFDiFr1uOzWPnP6JBg1sn263eyF+PGiJZ7Xl75AZsjEPjPrvHE1TJAAB4IW812j0e0lDy/VGbkBMXeGXEOqzto/LcXR48Tj4lH+AmKiSHirZgva5HXFUubN5bK8IaQo+WBK/M/DVc1a3uIJGwZ5MTZ2bcSy8rQkMlIApFCBwfuw3DCeasiNCqqiAs+ws34tR+/eA/GtaRwQUXuFddux3PdjKYOPwfs4TJcpFelKjR7Gtww96C8pB3IxvdbjKQfn/UJjZo3VdQjna3u1sB1CDJrBHnVZKlabBp6MqI7rj65h2ohOrF4WtQQ2RbsTq2NPU01UzlCICXCy08+rhwCQEjjzxGfpYuIWKm/BVPdPmTLV2JQyxIv393T0Ow1pLmsMfHbsOqMzf2TEtRR6y18mTijun0d64ndvh7IgGRDcWXMr9HzkzlEt/LUAmIQhEiuWZzyCZyB0dvxkEmU8K4P/cEk202yGJkFrmgHsDrtyCrLqUbjezjZiQ18Te8MDk4ejOeYZKi10asQ39VJX8a3W6oCVPlpfDo46KYSdI0ANYbhBuO7y4fNDfDYCYh0gDY1rkTbW1kgtZTcJsGQz6ZKWpdLjjjdAYljW4hoM3jAV0Ish2vcsxWbPJ4+lxFlJuzojg9bgfW6zpYqaqJbPf89vswHuYEUGvh5XQlmx49cu43L4n9gHq4Q/FRaE9T9BwqAVEoQmR/4cawqiAaABsIKwDGpczB004n6Znw2oh1yDWuHCragpNTU9nHrdf19natjkxI5g3BhieH3yqz/5KDNCcE8O6AU4Fpw/jnMdVoZHfOZQwxmcKqZFgM0bmfHynagqlM69wrI9YhJ8ywr6AcLYx5Y7h0p7WNmz/KNZshS9O8FVGjMaCiYTMYoOra7Vh17XbMM9MBZVIHlSxFMLoQ0MYkgKn9qgH2vMR/rpRiVgzREMHFJVyy1iw148Cjko9eh0pAFIowCKc1Z29BOepCKCUcCSZENknTAGDx2aXse/fAV2XkWqmNvqnfeXy+4Fp+Bn0yM0B+NWQm7kSHx0PK8jpKKnCU1UoeVle8k203kyFLbLm1O4/PF+dc3iRLpsLFva+d8dqIdcj5q7yRvx655G/PNc9hPvP+hEO4A/7TvnhSFDDnYTUYwDe8bzMYINcv0fD//EyI5Pvn8z+Jp2pPvNF03S4UHo2cuWu6bpdXmpcyK5UoZsUKmTiGKe1YDM9EoYhfVAKiUIRJee1ysYwJiLmg7e2RGzAp3tRa4ogfZJbhAbsdtpxbQe6cZ2kazDgZPOC8aehKLCSCxYeqFrAmg2OSkiCXaROyGQxs1aSrtHo8bEsF1brjI0vTQm7P4XbTP7BvFPdmXumbfvX8mvbHHZOU1B4onXE62ed8+eq1GG7SnG+xsI+rA7DeIA6Ph10LlXDnStqEANl31Sct/P6oTWj1G673nw15e+QGbHS7ydbL/YUbUYMuiij0VSbuRCsi/V257WE0pR2j1+6ehtJWpyjDeQ51ttYXiDehCUXPoSIhhSJMGt1uqNHpnn1dCDYJ4YZwFV5evnotHrDTqlXVbW1wUad3tHNMpqDEZVJKClCJCYC3NYuTjeWqJqHgKKnwBkFEFeTg6M1YnJxMDiO/kLda2lLWkZ9XPyO41/inS5fgexlPt7/Gj1ta2h25q9va4MXha9B3Pv/DlgIPVS0gr8tjDgcMYVrKZLyRvx4Hmkxk0rhi8DLMM5vb3cP98Zn6RYJw2iWv/+wJUd3WBm/k074KHWd1XhmxDn0D69XOwMDXl4RQWA0Gts1I4eW0ZCMgz2wGtlJpOwGhmJVGElkFhDWm3fOS6MmkKVZ8wMzAKfoeKgFRKMJkxeBlyDkcvzJiHdaGMYis8NJfM8Kv/HbrfWwauhLvzcyEim9WBq39NHth0PuaZjTChiErQq5kcFWTkJm4k33u6rY2qHQ4yLVNQ1diVwN+u9vNupy/1xToMr9hyAr0PWfHQHnXsNXoS0g6ogsR9szCnmueYw+0ezzse51nNsNjJxd2O4B8f9SmkE/84OjNyLVfAXgHzTti93hg7KezWelmKtl8e+QG1IVQ3iCdoAtBtmIdH7sNdSFok88pc72eIj2QhMiqbg2cDHMcDM/HgqprtyNnyiqFUD5TJDYqAVEougnnxr3fbme176udTsCPHlY/qAy7hq3GE0xw/r2Mp5Fzy3UIEVDi/8OlS+S/G3nkMcFJrBYefVx0DNy7AxpoNZ9DRVvQ7vGw18EZl6tL1wiXxAAAHCXW3h65Aa0fPyKo46wfPyKocz09bgdWXbs97Ja0T1payVasNbnLkVPienH4GpQZG3aFQZ/MFJwYgIwHvioTJibhGv/pbEHtcPuMLqkr80jRFuyvaeTMx/7CjZilaWz1SXGlCkIl5XXFO9FqMNAVOp//BVGFjCac4eaQT2YKtq2v32deD5RezvWfPSGartsV+vd6cpkaQu9lqAREoegmXHuFo6QCuX5fMWF3WEpafYmlg5fhny5uIN/AERYL/GfjelIty2owwGMnF4pbj81j5z90IVh38AyjEeqK+cpFqIgJuzGbGQQXE3ajrLLQlarDYSaAN338iOASNTfQVZ7OpFLD2rkEbxWCkwIOp0IRCtwsj2zG5ERbG1uZydK0qLhun3H2/vab7lBXvBNTmMpCXfFO/ntUuhKtxvCECMIl77JAQUeyNC1AsMAfU1Jdnwiy483rR9FzqAhIoegmYsJulJb5md23wWazGkDthF9eoE2y7u2/gJ0TeXH4GnQIASOsFmhiksN0oxG4Hf1JKSlhni1PcXIyWykrTkoiBzPFhN1oRZQOpN9ybJ7gBunHWK1sL3quyQQ5TFJkQsYEDrzBQ9y4TXcTyuHcR47JxM6OGBFhf+FG8tjBJhOkG43kjNeea55Dh8dDKo75pIn7okFhKJgQpbLK0mszRi0813/2hGhkEn+b0ch+J12u0NXvEpF9YShIKnonKgFRKCIBMzwoJuxGE7P7VjlmK46X+FAovIEgJ72bpWlSWd5jjjZ2/iCdCb7nnFokuN3L7vD2yA2oIZLeFwdHb8Y8i6V9ONyfput2IXeuAN5B+mLmGhqTlMRWcvItFpjarx+srVlGPue9mZnk4HjNuB2YazYnzMxCd3x33h65gXzvqPkP/7X9hRvR4fGQ79H7ozahzWAg39tDRVtQNrys8LayuYQgk5Cqa7djutFIJuyOkgo0pVax3jyRpMnjgVomAdEvXx/UGmotUT0vhSLeUAmIQhEJblyP1JAkAIDLzQcVtS4XGZQqvKzJXY66EPAfDeuC3qN5OUuQCy9lu9vLzi4VnAdFNNsDpvXrx65xrVK+NU417YzTyQbKsvatyamp8OLwNfhxCx30rBi8DKvb6KRaQwRjgpvo3fD5HCGrPnI9/ADABpcAV66ft0duQE6dKcVogPOMkpvNaCSTE8UVKsdsRQ2ATDR8/ioUWZoWliJaqDS73WF5z4gJuxP7S6VQhIhKQBSKCJHP7ZxP3IncMPqLw9fgR0wQqPAyKSUlSLHJR7qmkZWDW4/NE9rl/99xzWIwACdzm2IwwOyBi6MSCPh8I6iqwwt5qzHPbCb9T44UbWETLdksQkepWB8PfFUmfPMvk2022EE8J4B3FoZSodpXUI6jrFZYcGZJXAfKtxybJ7hE4v1Rm1CmzsVdbzLGfzpb+M9x6EKQLVW7hq3Gel0nB9LfyF+PqQYjcP5CCi+Hiraw0sY2gwFu+HwOWb2zezxRr4LU6jrb3ikjFtWZeECq7veX5X3iPVB4UQmIQhEhdCFItSMAACPwA7wNuk5KTCq8fC/jaWz1eODf6lcHvUezBy7GPLMZymsDb1wjrBbQgW7Bueh2wz+lP0UGCBlRboF5e+QGHMTMbMyUJD6VDkdQMDv+09mC29G9qXKu4FqFUowGyLs8CDtz4GIsYWZeXhmxDgczA7Obhq5kZabjhSxNg4OjN5Pvqcz5/frPnhAOZgD9zuPzBTc70/Fae3/UJtQQySTY1wdPBcobhqzAM3H+3sYLlMHj3svvLZX81Yzb0elcVXdxSYQcZJVOWdWtN8EJCSj6HioBUSgixMHRm7FNCFJ611FSgTOys8njHCUVGAnju95M2aClyAWFhVZre0Dt44YUG+wrKMdXRqwLuqvLjLDSYnBzzDGZ4A+MutcBu530ODlUtAWnpaUFtGLlms1Br9uHLkR7INaR87oblg5e1r520e0mW9wAALI1DX57gR7etXs8Md+p//6Jp0RXWxZl7TacwSCAt8WMa19zeDxs1cmIGJTwpBmNbFsfdz0DeL1dEmXOpqeounY75phMpJDH+6M2ISdPXXXtduwpJSaZC3pfUYdqlrSnYfKZGJ6JoqdRCYhCEUE0RFZdqNBqhbNt28g7kGxnTOHF7vGQLUrfSZuPZ1yugMCda88Y/o9ZrELN8H/MEvPPRP8G+N20+ayC12sj1uEjAxaR0e+KwcvQX/Gq2ulkkwxZItXxWvtu2nzc/s03pBLXT7MXYqXDQbaNvTZiHXbXsJFrO3moaoF44KsyskXJdllmubPH5kwVAYBVn7upcq6QJSB2j4eduaHwtXlRkr/vj9qEOSYTOd/zyoh1OMJqkVZqFACNus46yTuYYXUAb4IXrqR0d5C1Zh1hZKq7QiK1b8mqe90RjVAkHioBUSgiiKukArcPHco6dXOeDZwyiuIK83KWIJc8WBHBN0tTePRx8XpDA/nv7khLY/0d7khLazc8izYTkpPZKoiMTUNXItW205GLTAJ267F5ZNvW3oJy5NS/uGFqH5FwK+/Ii8PXsL4O3Q1S5pxaJDprtQvHm4QzLvQlTdRwuawSs2vY6j7tFdSVNqm9BeVoQSRbsVwlFZijaWQr1qGiLdjk6BedJOS/fs0/5h93RyVRkFVWEgnueudamxWJTd/9dVMookS9rrPu1GecTnI3GcB7w6V2oRVXeLOxkdzVfGTAIvS1Vo2yWqGOSVQ0xHbH6o5wcrbR4HsZT2OlwwG/Or8m4p/3srNLBSeIUGi1wlRGjcvudpNB8orByzDVYIA5p4K9bjYMWdGtKogGvHeDLgQ5I7Ft2CrUEDt1DpclajnMHI4VMaxe/Bs+nyO4qhuAt3LiZs5GAyDfW995cgpovR3OuLIje655Dk+0tZF+S3sLyvGjlhZ6xu7G9ZhjMkXee4WRZI9W8mH9+BF2ZikeER56AyH17zNY49TUPtKe1tdQCYhCEWF+mr0QP3c4yADpkQGLcJTVSh53qGgLjlO+IFIOFW1BTkLX18IwITkZKsdsDQpeHqpawAaJP69+RnBtUdFi9sDFWM24sQMAO3uhCwHjP50tuDqAhsiaKZ5xuViVr23DVuH/7JdGHjdz4GIck0Qbpb04fA3KXMVl+CpOlPHhy1evxVqXi0xQNg1dGXbiU+tywaahK8n3IMdkYnvxH6paILjWts4c61/IW40Oj4dsc3tlxDq0GY3k2oYhK1ADUNK8nVAzbgcOt9LhTJ7ZDFMZ1bsck6nTzy5kbCdCP6YbyYmjeTA0tWTRix/EobHlpdHkn+0eD1udVv44vROVgCgUUSDPYmarIPkWC/xn43ryxrCutpYcYldcQdZu8Or5NYLb3S6wWlmp5Dyzmd0VjybFyclkux6AV/mKkhg+OHoz2oxGdhg6z2JhFbVkXiQA3soMt3bG6WSH1SenppLzOV1BQ5QGGFyA2Kjr0jYdro1KNm1l93hYDwe7x8PuxGoSd3QfPhlmamNixeBlyF1/WZoGg7TYX5uJCGdQmGMykQne/sKNaPd4pO7qIfHBEwI0ZiPDY/b+H0Xyqe49L/e4XDUGAPCjh3vmPtPvM/LPovFaEI3XkmsnJBs1isRFJSAKRRTYNWw1aohkUFaU/AS+dP48eVzD+OfVLEgn+AI5iv/b0AC/v3iRXKt2trFBu81ohDW5y2P+3v9T+lPItR0svOpZ5BImKyIbYL/Z2Mg+Xy6jmtUVlg5ehtVOJ5kw/Uv/BdJqjozjY7dhcXIyKbPsG9qlKiR7C8qxVQhyTdZWw1WOHviqTGiI7IyJw+OB1whVNQAASxd30W1GI5tQtQoPUIP324atwmqnk1zrKh1lqnsjPid06rPfNHQlOoQgk5AjRVvQZjCQviyhgloL72iu2QGmPUh/+OYLYT+nKeUswM1LyMdN1fgqYU/MF1k/fkSYjHSCX5jzBcDUWeTrEParo3peip5BJSAKRZS4Oz0dTjBVkHszMtgqCACoKkiYlNps7G76l442mJdD36h70tPitMvJzgA8MmARfunYSq5REri/vbBW3JuRQT7PsrNLRVd3Ern2r9mSViyrwQAfN28K67p9IW81NrrdpLnh8bHbMFTnda5KdsuxeYJzMteFAA14OVRZwJbdxcH4F/JWoy4EqYq1a9hqzNI08lp4cfgaTDHSczhdgbvuAbzSxpza1j9/+XRCzaXtL9yI3Gf/Rv56tm0vS9PYxDQUbAYDm4ymZh5lj8sOf18gbGnfnqj4DjKZIJd5Xs63CACklRxF4qISEIUiSszLWYLbzjUA/C345v6DzDJcdPYseZyrpAJVx2t4aIis/OokxmvlX+tWsb39sWDXsNVYaLWyff676uqC/mYzGmHp4GVBsxcaIqtapQN02eX9aGsrGSQDeFvDKCGFNbnLce+lS2TbWFeobmtjE0E3U+moHLMVjYhBa1ybVaOut5sAdqRe16HB7YYX8laH3LoVCi8OX8MGya3CwwaNJuhege6Br8oE1f71Rv565Ko+r41Yh4XMzFq8osMVBa3Co4+LwqOPt79mDZEc6n975Aas1/Vut2INNpvBwRgRypKBOkf4GYjQ+blBmWJcIg2ucxUeRWKjEhCFIpo4MwF0OvA90dbGOqAPMplIaUlFIB1lNDefO0f+u8Vnl4oUvx1s/13dA3Y7fNzCtE3EiEOtLXBDCn2dVDocAX4QH9g3Cp98qxUxoBKSZzbDv/RfQN6suWochc+okJLY/Wn2QmxjgpdzLlfYgc2moSvxRFsb2ULjq4L4B5M+KsdsRf/Wuus/e0JwSlatkl3Wel2H82H48fy8+hnBtfZxNLrdpMfHrmHeCglV6dg2bBVmaRrsCHPW5uWr1yKXnHNJFwAEmFb2JDJjRv+1I0VbMF3TYNoXT4rKMVvRXx75jfz1WM/MDh0q2oKNug5DuuGpId3Fl+HICfcppdWBM05+rSecpxxCsF4o1ZJzVfROVAKiUEST0stqO38J7sFuum4XvpSXRx5Wde125Y7eBawGQ7sJ1/B/zBLczvIvz5+HskFL2298/m1ElQ4H29sfK3YNW42lNhs55P32yA1oMxjaKwuf+nnJbBu2Cu/v37/9f8t2PENVWMvSNHAB/X4e7ZAU+fCpS4XbtiNroekMX3JiNRjYWR/OqwPAG5Bx1aNbj80TTcxwui5EyD4Mb+Svx0Krla0kZWkaOT+28KpnUQdghQt6M5zpJrXmq3L96Mung9Ti9l2eHaI4PW5Ht2SlZa2CUnlna23Yz5lh4i8FTvVv/KezRU+Y/tVIfJbCkb9WJDYqAVEoepB6XWe9IDREcjBXcYW64p3ou6lNTk1ljQQ73mwXXvUs+nZB48WFfoT1ceTmV16+ei3mXR4gpxLTTy7PXnzEVHI+ad4k8kIcQF+TuxxNgORcxmsj1mG60Ui2W20btqpbQVwr42BdOWYrtnk85A71kaIt6BYChnwyU+hChGXsKfMAcQjB7m43ut1S13UOmeiBzWhk1bg0AGiQeI4ovAwymcBqMLT7ifhXSUyIrFCB/6ZGqHCVQQAAC5MUp/59hjCZaIParjBQMsvBeW60CtEt5/Vw4Tx/ALwbcrE8F0XPoxIQhSLa+PpXiSrIT7KeYXd8Vwxehj+ypUf11HoL/1a/WnD+IAAA8wYODPrboaItuOPcCjGdGdruCXQhWMUiTlp3hPVxbHS74Q8XN5Au5wAAHzQ3d3lQ2p9tw1axSlxZmsbusHLD1F2hcsxWdHg8ZLsV174BAO0GkzJBAa49bPg/ZolGtxuamHW7283uwHOJQldwM+1Wswcuxka3m1ybOXAxDjKZ1OZEJ/iSQp96mH914kjRFuyvaWQVr2bcDtQAwmrF4pIMAF6owGowdEuRivI8amfiTnLtYg8lsDKzTkXfQyUgCkUPowvBujprJoNULUvhRReC7Wn+tGWzGMkM0hZarcCpOvUE30mbj9+StN6V1y4X71y6FPT3b/d7Eht0ndXLzzWb4Ttp88PaYczSNDLY3TBkBY6wWMje/BeHr8F8iyXsINmIKO2npwbSO2PIJzM7dYzmgrmIm9VdZuFVz6Ld4yFnbTYMWcG2Yv0k6xlUwRyNf9L24vA1mG40wmMnF4pUozFAynjPNc+h3e0mr9/T43ZgqHM9APwMyK3H5gnuGtIQo3Z9cXQnae4OslbFjEO/4BcJl3tF4qMSEIUiFty8BMFaSzrT/jR7IXKmhQbjDNYnQnGFA3Y7cE7mTR4PlKTMIe/wVoOBHdruKSbZ5iKl1AMAcEdaGtydnk4eN8pqZec8PmhuDvt8vpfxNNa4XORcR9mgpXgHcz4zBy5G2RCsDF8VhNqFPj1uB+aZzaQvRrqmgcVgCEvNSBYcyYLR7vbSGyVXX5amsbvjY6xWVQUhsHdwnN82bBU6hIDXRqxDhxBd9lOxezxgDVEOnZsByTGZIIergCBCXTFdqegK4VzridbuxHmHKBIblYAoFLHCkcMqYjW43Wx/bKXDQe6QKq6Qazazg+SywePTTmf7/EQ8YUUkZXB/eeECvHzhArkrbjMa2R70om5KqX7c0gLcXIfNYGDnmLI0jV3rjNPjdiBnnLi3oJxsXbQZDJCEyHo6cDNCnQ2SpzBJQCQ8MnYNW41WgwEWE74ujwxYhI1uN6z6+tmgte+kzcczTifpB9MX4IwDX8jzmsB25IGvysRrI9ahv5Hp3oJytBoMZFJSM24HuiFYaY9DNjfCiRsAAOtb1CU+eEIcuUi3SMq8pEJNrCJFk5P/HZJV9FyMtLEisVEJiEIRK25egmBwklWQyjFb2cHdNbnLkRsuVnjhAkQAgA+YysifLm4QGZoG45jqSE/y8tVrkRqIrnW5oGzQUuwYYOuu50WKwQC5ZnNQQvXXpnJxfUpKt87ntRHrWJO8n2YvxA/sdtJf4ZEBi1AXIuyd+lyzmW1PzLdagwLzHJMJDhVtQY0YMk4JM9ArPPq4OMfMlTS43RFpn9kwZAXqQpAD//NylqARkVTM8qli9UU4PxcA7/fH/3/7jC45DxCrwUBeZ66SCrQaDF0yhpV5CTW53fAKs0HSrc9PtwF46CRdpioVzjxYtBHOdPLv4QoCKOIflYAoFHFCva4DfvQw+WNr93hY1RYFwFhmjuNLx1bBtSwddTjad725we+eZEZ2Npxs2xZwXr6gIsdkgk9bNrevXQQPZGsajLA+jh319N9raoIR1se7HSW/OHwNTmASmXpdZ1uFNMSwvUFeG7EOdaZtZsXgZWg1GAI+O5/S18HRm4MUxQaF6fzc7HazSmlnXa6gYDdcZImMWwh2cJhLXBSBvD1yQ1Di7sMqmcNodLu7JLOchAipzHdA5nERLUPAwRLVu56Q4O0UJpGSSfcqEhuVgCgUseTG9QgGJ5loiAm72R/aI0VbkBsw7uv8+dJzrPGchgiDLY+Ri5NtNvin9KcQwLvLHMVTDIui5CdwiHYlaF58dmm7dv8PMstwmPNKgF3tdILRMAMBAPItFrio72y/vrgELByKk5Lgz5eeC7p2XxmxDnPNZvjXulVBa//SfwH+/uLFbhlrphj5W5V/4OgfWOWYTO2Jy63H5gmuB3/kkcfYAWEfXOtWYwTns1ZcNvyjqkwLr3oW7W43zDgZ3Jbnk/Ol2rQUgWiIZNvctmGr2AqJq6TC29L13lrp+9vodsPB0ZtDVp3qVjJgcF7xmupAhqQiI3NljyZWTm74ne0CnJn0WifvuyJxUQmIQhFj0MAHLcKZDvC3YGlIAG8VhKuQ9GXurapiFZ7+d309exw3tB5PvN/SDPZGbxXEihjgSq2ZDfClY6sA8CYGPoqSn0AAaE9CDreG7zHQkaLkJzDdaCTnZlYMXsbObLw9cgPrcdIZvgoD1SIzL2cJtno8sOzsUrHq62cDHMlfvnptuwKSEZH1zsg1myHHZGKTjFiyJnc5VjudpDLTtmGr0M18+9fkLu+W90pfwXctURW110aswyZGFctVUoGg2wDe3RjW76+sghItBSzZ9RCtqktnZHDJlsEJMGUu/UZ4zKSEvSLxUQmIQhFjxITdqCHSg4A3rmfvRq6SCuSMpfoyNW38vWlXXR3597Gfzhay5CRe+FbqPPwrOuAPFzeIyampAWvW5JntQX3HJCNNexTX1tbC4rNLxdEIJiAA3t3TsVa65S3PbIaPmaH+LE0Le2h717DVbIDt73DPyYsOMpkglUmACqwWqRQzt0Mdjk9EV2h0u6VDy5yKU63Lxc7LKK7wyoh1mGI0wNqaZWL+6cXC31V+zzXPoczNHDR+00L2mcmSjK60d4VzPtWSinlPSTiHlfg4M694aSl6FSoBUSh6ACNI1Jk0u1z3XGmiX0HyXnzcvEn8ICOdXMvRtO4Nf8aQNKMRcjQNihzBAXSj2x00J+JjRnY2TE1NBZlBYziMsD6ODR53e/XFn4Kk2fjqhQusN0itrsOdx+eHdf3maBrZggTgNYCzGgwBFSIAb9Ugx2SSqqQN0kyQw8yHLD67NCjx85GuaZAehV5633D1DZ/PCXqtL+R5EzEqkXtx+Bq0GLBHFPO4zyWW3FQ5V3BzctO+eDLg74M0E3zc0gIbhqzAaqczKHHr+O8BAGDqLK+IyIHQDTa5JMP08SOiO621GSb+VGQzIOF4nHQX6TC5tTaGZ6KIF1QColD0AI6SCrQZjfQu6sSdiAadVl5hnG0VwWRpGuwatpp8v5696io4UrSFXLtwLr5keSfZ5mL/Ghe02IOVmEZYH8fqtjZS8WaY5TEck5QEP8gsi/g10980E2ecPEmuHbDb2eQu3Whkg/3O2DZsFX7c3EL6HpQNWop2t5scxl541bPS13/G5WIrJ0dbW9kB9CxNY6+h7rK/cCNaDQYykXsjfz1rkmcC7FZw+a91qwQ1x9MZ3Pcsluwv3Miew55rnkN/1/Olg5dho67DrcfmiTW5ywO8lvZc8xymGo3kLA6UrkRTUh0ZTMvczLkKiKstDepaeOPRzhgo+S5xogvjP50tonXdyqgZtwMbxj/Pt1kp+hwqAVEoegibwcAGYxZE4Drm2eSkD4IGnU3KZAGBbNfRmhxegBxNPu4vwDCYbxOqZWRibQYD6K7no3KtGBHB494V9Njvj9qEv8/Ph2Z38PO+kb8ejQhh79IfHL0ZuWShsxkIKjn55y+fFvW6zqoU1es6q3I1ppveKp1hQWSN7ThlsG3DVqGGGPZA+k+zF5L+KokCNwQO4L32/P/33suVpluPzRMdW5J81TKqEuIqqUDq+8Z9VqaPHxGs87jBGbXg28KcT1yKmZgv9PQZKHoAlYAoFD3E8bHbMNdsJo3aHCUVmK1ppDmhmLAbe0bDJP6QKci8eoG+qf3p4gap6lFy6iyMN0WhdKMRXjp/njSdG5OUBJNsNqh1bg9Yqz35nGiud4C9sQ3qvw5veFbGnmuewxrdRQ6kpxh/gSYX/ZS7htFGcaEw8shj5A60zWAIMmlccGaJ0IUASkXKZ0DYMTj1IfOXyY9yArLnmucwW9NIZaaXr16LuhBkK9aa3OWYbjSGbQD5yIBFPV7NiBVGRNCBrp4cbW2FT5n5KaEnB7V/cjMXrrY0aHL0I9eGJLlYFauukMR8j249Nk9wyluyWRWFIpaoBESh6EHeyF+PP0rPINfSNQ04VSGArjv09lbuPD5f5FtoF+AHvipje6tvsaXCv/RfIL3pL7zqWfy6akPcvL8WgwHKBi3Fjju1P/ryaXG4pQVSjL/A/25uDlgbkGsDgwFBd3lgjuObqJxXra6zlQej2QBnmfkUm9EYtndF1bXb0WYwBOxO//bCWpFvtcLCq57FgR2SUl+SsW3YKkzCwFteutEILklA5pas5Um+m5Hi5avXIif1+8qIdcglQema1r3h5j7Cnmuew0ZdJzd69haU4+nmFFoGdtJmtBrd7cdlH35UJDHJKpob2efv7izGIaaVqtHthvdHbSLXSm3ht3x1B2qmqR3VWtwnUQmIQtHDCOan90jRFswzm8kBT8dlh96+jMPjYduspqWlwaah9M7iXntT+393rBr4Y8mN7PB2uPzoy6fFl5eTqXpdh59XX7kefpaV1S41/HpDQ8Da01+fhfTsJzDrqrmYYzKF7UYuoyRlDk5KSQG3J7gVy2iYgaddLlIVa8XgZTg1NRX+2lQe1jllaVqAqpVLiPZqWJLBEFApSjcaYcOQFej7b//2rzNOpzTJ4IbMd5xbIbi2t0ijA7AmpIVWK5nI/Uv/BejweEj39L7CeGJWiOJQ0RascbnoIekb1yN4zKT8rqOkArM0DQqPPi76axo7j6EhsuqG4c5DdYasFU3mIB9NuIQIAMi5LkXvp29HMApFHGA0zEBy4BG8AVPHXVsf/TWtT7ujj0lKau/j7kihpD1mz8WL7f+dZDCw0qX9TTNxR4d2np7gjvT09nN++eq1eH9mJrx6ub3GajDAK5f71V++ei2W5eS0H/fEgAHt/71hyArk1Jy6i9EwA1sv0cF4EiI74J1iNIa9S+/73H2KWsccDvjuZS+Y72U8jTa/FiT/KuLSwcuw0uFoP85mNLKmbKu+fpatsNXrOusrEmn2FZRjk8cDhUcfJxONel0nW7EeGbAIz+s6PUzdB+CqAxSOkgpsuFxpIufrGJWmRrcbMoxGcAsBzcz1IJtFi1USGw/cQggq+JAOxdvzo3I+ip5HJSAKRRxQr+vkTua2YatQF4JcqxyzFfOYAKkvIEsyuMmQjvMBadqjeMbpZHdLKx2O8E8wQlQ6HAGD0P6KPR1bslIMhnZZ3o4taGecTvjDxei0lSWn0hWWcSlzkDNCnGSbi3aPB/6zcX1Y5/TaiHXtLWkd34csTWt/n17pMAtkMxqhODkZ1tYsE2OSkthWqlyzGXKZHeockynAfyTaVI7Zyj7XaadT2srTLaftPoSjaTjAX5YLV0kFBlQ8psxFq+Ui2fJ6fOw2tHs8oAsRlpFlX6piy65C/8ptENNLVXtWL6XvXP0KRRzz8tVrkZJSBfAGTJyBU63LJe+t7aU88FUZO0j+/RNPibW19I7l0dbWoNasel1nZTK3DVvV4ze/SSkpAf/7B5lleNblglfPrxEdB19zzLPw9YYG2HJuhegY+H8rdR6rINVdDMYZeKKtDaiK0eyBi5GTdv2n9KfY674r2N1u2HJuRVCl4idZz+CbjY1ww+dzRHOH784b+esxz2yGUVYr3J2eDsUSn5SZAxfTCmthurp3F6oKsuea5zDXbA5KrgG8lS+b0Uh+LooOTC7D4enfeFuxOrhyO0oqEICeuztStAXTjUa2Gi0z/ZNVRzojXFPPnoKTs1b0XVQColDECTajEahAbU3uctSFIKVL9xduZB2iezMH7HbY/A09WH2ira29Lcmf6z97Quy3BzsHHyragnkWC5TXLidv6J+2bO7RG/07ly4F/W1ezhJMMhiAqoDlWyww2WaD2UTwnGexkAaCkWDF4GVY0iFZ8nG4pYVtBXq1oQH+FGZlxtdmQyUE+wrKUUMk27x+mr0Q88xmyNE0+EnWM2SSIascpPdAAlI5ZitmGI1wU+VccqaGG1afPXAxqtCva1Rdux01AHIovem6XcglnlaDgd3E4JKMwqOPi+4IBchaAEkjxR7mjKTd7DQjgw0AbIusIvFRCYhCESfMy1lC6sv74BINh8dDBiW9mRqXi+0b5qpFB0dvxrFJtJfGayPW4WCm3eaMywW/vUAo4cSAWud28cTAgfSay0Vq+n8v42ksQDNcurAl6JxLUuag73Ejfa4AANmaRrZ5bRu2CouTk8l2qxfyVuPh1tawvUFevXABXjp/nlybkZ0NiwYNItdsRqNUkpRLMs67dogxzHUUbd4ftYmtYi0dvAwXEzLNAN7d5/mnF/ep34hw0RDZ39osRhq9/rIaHCUPzQ2at3k83aoKZEiS4CExUGgLFVklaM81z5G/5eM/nS1eHL6mx6vQiuigEhCFIo7g5j22DVuF9bpO7mwdH7sNZX4FvZFU5ub7z18+Lbie/gVnlgjZ3Mi9jDTvd9PmY47JRAb00abZ44FhlsfI87J7PJDFBPxOhw4eN326WZoGWVFykhlhfRxlcxP9mapCpcPRqZEgR4PbDWecTjIwrHQ44IzTCVRgrgsBVkRSievTls1iAtOadbS1lfV8iAU68ApPlNcJwOUKidudcG07PUHVtdtRF4Jtt7IZjWTb65GiLagLEZCEFB59XMjMJLuD7LesJyp0nSF7vdmHHyUXVeWud9O3ohaFIs5ZOngZcjcWh8cD2UwAl2o0tiv79HZGHnlMcG0N+RYLcLvTdrcbVgxeFtZu2pCvdWhIif3PJVfNAfBWzNKNRkjrEGz8+dJzYh86wJZuISsdadqj6HEL0kAwEuSYTKQs73fT5qMJkWy3eiFvNQIA6e7dGakGAxwfuw2pakaty0Ua6y07u1S819QEey5dIl3QG91uVoDgaGsrfLvfkz22K3ukaAvaPR4y4do0dCVWtzlJxaEX8larVqwu0jD+eQQAyDj0CzbR8JeO9SUZVdduD7guUg0GtlLR6HYH/ftQqNH5ajk33B6u904k4H6zrZTq2GXOSFqzFImPSkAUijiD60nfW1CO/mpI/rxGzDz0VtIl0qlpRiPr/9GdtpmhBU/j0dbWmLdideYY/k/pT+FIiyUg4C9OToZ8iwU00y/wottNmgGarY+yJnbdpb9pJlY7nWTyU5Iyhw2CXxy+BmXGmxz+79GQDl4OIy7PyExITg7wI0nXNGhwu6FO16FR1+EDe6DPQ63LBeMut6t15IiDVvWKJbLd5LZOdtZVFaRrWA0GNmh2CNFuYDnok5mircNGge86bHS7+dbZKBpF1jHJyZrc5XF3n3C4jVDH5Bl9cb6xL6ESEIUiDpENPnO98m+P3MD6ifQmDtqd7PzHbxsayGN+dX6NmMAMSHeVf0p/Cn+QWRbTG3hX3Lb7m2aif0CqIUJR8hMI4K1GDNLoligrIjS7n4/K9aIhwh+I4XkAbyWES+QmJCezYgAUhUcfF74gpWbcDkzxa4959fwa4ZPK/V7G06hd/huAN4A3IULZoKU4c+BiNHVI9GTtLUxnW0ypunY7crvn+wrKMd1oJFuxXhuxDq0S7xvFFeqKd2IKo27l+/254fM5wtZhAN1Xjcs+/Kio13X2t6q7TVLnXHQqv/jsUpFq4FtUu/m0YcNtpqBBBzTQr6U7KmGK+Ed9ugpFHKIhwn80rCNvFtVOJ9tuxbnx9gk+fFQctNNbaQ6PJyRPj3gxeLQYf9GlhOeMy9WuoOavmpWmPYqHHK3knIjRMAPfa2qCtigkIcMsj+EZp5OUhgXwqphRifQPMsvwaGtrl6WlTYjtO9EAXqUoX0Ly28bGgH97uLW1vd2qXtcDjjvjdLYnJwDy3el47K/vyBv563HXsNXktfPi8DXYlcRW4b2ebEZj+8yNv9hH5ZitqAvhrch2CJTrineiFVGqpMbNsXUVTrBkxeBluGHICvKz70kpXK4tzGYwgJiwmzzf7s7JKOIblYAoFHFIQdJs5FyYrQYDcAOyxxyOXt1iYfr4EcHtlsn4adVZ+BdmyJzi4OjNcdeqIGOE9XH0Bdffy3g64NxLUubge01N5HGTU1M7bfMKl4VXPYs/y8oi1zRE9nkrHY4uBx42gyHIpO+M0wmpf58hjnTwQflJ1jP4aWsr3Hl8vqhua4Njfgnp9zKexrMuV3tSdJxJVsNV6uopfuWXVPmTpWkhVZr6MvsKyjH9ciKxv3BjwLWmCwEOIUjX9dPjdqAsAWnqhifP9088Jd4ftYn8At0qcRzfV1DeI79rtx6bJ2oYo0bZ70+TMzptoor4QCUgCkWckn3BDbUnnyPbKNI1jZxHeHH4GuzN7rpWSeAKAAATdyZU4hBJzkgqY2lGI6ytWRa0lmL8BbqdHnJOJBI0ud0BlQUfG4asYCsd74/ahLoQrNKTP1SLRsP459FqMMBFIsB7+eq1WOtyQbXTGVQRm5ezBM+4XPD9E0+J/7x4sbOnTgi4pHvmwMVoRWQrVH2BUKuclNT5oaItrP8KgHeGoeNcko/uVEBk5p17eyjJCBdZRRG1lhieiSLW9N5IRaFIcAbkzsPkVLqlavbAxaxa1uTUVHbnM9GxGgzguuxK3BG2MvIhLfHYHeLR6OvF4WvQ4fGQCkhlg5bieSZQMlsfle7Udocc8yx86fx5MnjbW1COr199NXncoaIt2BXRgJ8yFZb+msbOz1Q6HHC0tZX0UDnR1gafX16nqHY6gWtv6Ul+e2GtoBI9GTMHLkbKrLKv0FmV018adl9BOdbpOunz4WvXoxSzzjidcDqMnf/OoIxW45lSmy2silsitDsqwkclIApFHKOZjPDRn2gDseFuOmj8SdYzrBdDosMFldmHHxWxvFlxxlk9jUMIVrr3jrQ0dsf7lxcusDNH3aXZ4wHOpyblkpuU7AXwVitkj3u2bZsYz7Qi/igjAyYkJ8PPq4MHsQebzZClaWT15EjRFqxua4OjTAtWKHNEseQHmWXYmfoVx7HW2PvbJAJ1xYHV1MoxWzHUGQpuiHrQJzO75YKeaKRrGszLWUJ+n2VSxL25mq9QCYhCEdckp87Cfpl0pSM5dRYe/gvtfAwAAbKjvYHfXljLuoLbDAb2ZmUyuvtMa9b+wo3o8Hig8OjjQZ/9t/s9ia83NJCtTT/NXoi/vHAhaue0Y+jQ9iF5f9Kzn8DWS66w1LgGaSYYjnSibTEYIIupglx/WaaYm7EaZDIBl8BTniHxAiff7YNyoQfwVn1iLS+dKPxb/eqA96Vh/PPYsaXKl2T4vEP84SqLGsjbqHob4apZ3ZuREeEzUcQTKgFRKOKcqqvNsIPZuTYYESo/Dl77dr8nMftM/AZL4aAhsj3t9/fvD9yQY1/aaQTwti919CXwIfMlOOFwkIlLJBhhfZxtGbSlP4Zc1eaivlNwycmHrS1gtj5KfuZJBgMsHbwMR1gsQYF3utEIhVYrjLBayLmYu9PTId9iCRJzGPTJTNYAM16gkjwfXIXQ4fFETYgg0aGSuo4tVVwiO+fUIvE/bLT0t+Py8Hpf4afZC9kLjDIm9cF5Oil6B/H9a6pQKOC7afOxP7OTdu23nsWrhvcj174ebJL+uCcab3aQVfXnwf79yb+n/n2GsPTB4MpfOtQfn4u2f3+7j0NFW7DZ7WaHZruLLgQ7m3S0tZWsgqRpj+IZp5OUEb5W8FKyAy9/X+7tvwALrVZwOna2H59jMkGWpsGuYatxbFJSgCP8l46tYkJKCuwtKA9KmHI0DcIxSowlP81eiFRS5eOvTeWk9LHVYGArJAq5eeO9mZnk34uTk2F8UmCLoG8jqdRmC2rx6qukSFpn55+m248VvQOVgCgUCcCN9Qb46igdvNUlI2lcOMk2F2U/7onGk0z7FQCwMwZT+/WDQb10HkbGkaItqCGSKlM143ZgTwx3frvfk3i0tRUWnFlCVuw2nztHHleQNBvTiPP9B/IVvu+np7f/9wjr4/hyk9egcsu5FSLXbIa8y7vWhVZrgOlglqbBHWlpAADwSHZ2wHzEEwMHsqZy8USSwUAqj30rdR7aPR5y1ue7afOxye2Gim9WqoCPgBv6XnZ2qTjDtOUVWK3wyIBFAcfNvDz0PzU1tVvnM/wfs3rN55Qh+S0qYWa8FL0DlYAoFAnA0IKn8cwJWhp0hPVxdjgyz2yG864dCX+zqv96oxjURL8Mp2OnyDHPIgOEfItFOuTYm7EaDGzvtRGRbLc6PW4H2j0eUtEnUnDtPo1uN6z6+lnyef+7uTlozc20sJxt2yaoNrM/XNwg7s/MhDvS0tp9dEZYH0fR5mkP2H9/8SKkad62rhzzLFxbW9vuq/DL8+e7+hJ7lNkDFyOXdH83bT6reHbW5WLVvxQ83Fj6ryTXyxFH997n3vSbVpA0m30tlJS2ovegEhCFIkEwW43wt/8KVvUB8PYUU+1WOeZZWPeP6AwXx5KzaQiZA+eQN6oj7mA5VR/zJFWT3s7+wo2YYzLBnFOLgq6LyjFbMcNoJE3LGsY/j9GaCViTuxzrdBf8M9HSsmHICixOTiZVmWYPXIw2o7F9xmHLuRWCC5ZtRmOQEpivB/3VCxfggN0ORclPtL9Aa/JM/PSyutXhlkDfgUa3G6ZdroiMsNK9/vHIt/s9yX6A++32gLYzH/NylmCtrsNDVQsSfsMiVuSYTMDNNn3UwntY/Le9OVqnxPPuxoT6XP90cYPoWEFS9C5UAqJQJAiTpq1Cg4H+Pf5u2nzMYXY9C0sWY/Xnia1yY5OU6T+w28m/N7ufF7Lyfl/g5avXIvfe5V6Wo6XQEGFQlGZBdg1bzQYV/5T+FO6qqyPX0o3GdqWz2QMXsz4hnzscMNjyWNBzfDdtPuZZLKQy1vT0dFhbs0w0eQJ3XN/IX4/5Fgv8+dJz4vpkeqA40SgbtJStmL42Yl3Uks/eSJ7FwgpjNLjdpG8IAF81iSqGxBIl+U7afHUh9nJUAqJQJBCN9a3ASe+Oslih/mt6l2tAri26JxZFKr5ZKfo30SpJHzdvEsVMn/BL9fVgMf6iT9zElp3l5Zh1Icj110asQ5vRSHpl+BTFopWE2AwG8nkBvEEdNSfyk6xnMN1ohA/sG8WnLZvFt1LnhfzZFlqt5CD5uJQ5eM7lgpGW4N3s72U8jfkWC0zrR4s9JCKy4O73Fy9GTYigtyEz8HQLAcfHbiPfZ65qEk2sqVUxf06FQoZKQBSKBGLa/RvR46FjA4NxBh7e/zW5JpKNYXktxAMvnT8Pt9ZVk2tnnE7gAtF3mpqicj49NQA6llC18rF08DI2oFyTu5ytgryQt5qVwAWInoTxi8PXoM1gIBOj2QMXszv0/5T+FDa73fDus4fYx64mHM59jLA+jnd/+SW5VpyczAaUrzc2wusSFbZEhZL39iWfKgnpnI4te/4sGjSI/Puqr58VnHSvQtGXUAmIQpFg/PW/TsLOsgfJ4ODShTbY88u5QWspxl+g+2JileB9zB4wAA6O3kwG2JwCDQDAAaY1q7tMSkkh5yoAAG49Nk/ceXx+3AVuOSYTq3B0xuUiJXtrxu1AXQhI/fuMqLyezpS4ZpykKyRnXC64qmwUecyOcyvEOYlbdeHRx0WdrpMD+PkWC9ydng4f2IOriMVJSVBqs5Fr8c7804sFJ81rMxrhz5eeI8UIon9miYvHvUsAAGxglNsAgDWzzNI0di2aqHFuRbyhEhCFIsF4bP2/4XWTB5Nr9/xiG7pc9I52v8zZ+PaLTyRcADXpGz4W2vzNN+Tf8aOHBbeL3lW+dGwVZ9u2Bb1fr4xYx84f7C0ox7dHbohK8NYdCdh/6b8AXzp/ntzV3ldQjrXMe0W5O0eKpYOXoc1oJJWvXshbjYM0E7mmIUIjIxlrMxqBa4p57ORCkWowgFsIuOh2B3mh5JhMkKY9iqOsVqh1bg9YsxgMUJIyB7+UVFfilQ1DVmASo4b2k6xnUFblUlUQGoNxBgIA3JBCzwWdbNsmipjfiCxNg3RJ61a0cLVmx/w5FQoZKgFRKBKQq8dksvMeAyXzHkYDwnu/eyqhggqng967G3nkMcHtoguPBmLC7m4Fz3a3m3UrzjOb4d/qV5OLq75+lt1x7kmqnU729Tg6kd6NlizvvJwlyDmzLx28DK1E4OzweCDFYAjyWADwzpb4vBY6MiElBXLNZjg+dhvqQgTJAT9x+jQAANTrOhy7rIrlI/tywGhPUK+M2cx7AuBNvDomXADeKogOwA5SKyBIbc3HGacTPmVU2rI0jR1cjyotQ2P+lAqFDJWAKBQJyIDceYjZdB/xxFtXIiVlCgCQnGoCA7MbGo9Uf75WXDN+IXmzrtd1tjUrVePnGrrKuJQ5mKVp5OzMd9Lm42TGTGzhVc/GZftKzbgd6PB4yJYqmfRu03W7ECB6SQgAP0SfZzYHrWVpGtzLBHC/v0h75QAAfNTcDPbLvgI+F2r/4LryctIxwvo4/u/6epj2xZMCwJtQvnl5/uORAYswzWgkJa8TlaLkJ5Dz0akZtwMblRcDyYyTzwjuO2M1GICrKlZL2kajym0Px+XvkqLvkjiRiEKhCKD5Kzvrjn7uDzXwx18HezxMvmc9TrxtCLQ0Be94xiO1J/lBcllgxCUHoZKmPYopjJLWMMtjSO0cA3ilTiNyApehfDPCwZdMUNQV70RZkhFNbxCuMvODzDLsOGAfjlneDZ/PER80NwfIn9aM24E+o8Yhn8wU/tfTy1evRV8V5KXz52GPX2JjQoQ3e+FAOocvWVMEcn1yCvs7Y0VkqxwqoVMovKgERKFIUIYWPI2DR6WTazffvRZt6XSFxJo8Ez/ceyqKZxY5rh6TSf597KezBed7MuSTmd2e//BHdz0vLpwLNm4D8LbmcOpiuis01bHy2uXsv+daPR74qkx8/0RoLXUOIcD08SP0OQsRNBsB4K2QWBGjpgC2Jnc5djXJ4hKQmyrnihqXi1wrtFphUkoKFHQwE9QBYPyns4WGGOQa3+h2g/XjR0StywV7C8rbg8kfZJbhUYdDqkoWz6iWqshwwG5vN7jsyHsSBb7Pu+mCHhZ/3K0+c0XcoRIQhSKB0RClgW7lx8EymwAAjhYdXt0UHGjGG2dO0C01D/bv3y4X2pE6XYf3R22K2K6tZvoFOlrohMZomIHNNbQUp2YKzYNkAuNn8lDVAnbWpdrpDAqcO8NVUoFZmkYOGDddtwu5xzs9bgd2plzVHV4bsa7T92vIJzPF0Q7zGT5qXS44yyQgxcnJUJycDGOtgYPBR4q2tM+ZVF27PeD5G8Y/j462NGhqyQp6vH0F5RhOJSYe4LwpFKEhG96XVQur27rZgvVOGNVr1X6liENUAqJQJDBGwwzkAt0bb1+NLie9cz7t/o0oa2+KB9yeXaL4pqvINVnQ3RYF74qGgSZ49Tzd7tbS5IIzJ9aTa395s0wceb/zgXRKCtVHgdUKL1+9lvyMKx0Odk1GmtHIto9kaRrceiy4fQ8AYKDJBD+KUDtYONTpOtQySYbD42GVwhp1HfZcukQOqL8/ahNWXUrnn9RJV+FESy7Au4kny6uIDLIkQ+ZBlIhKagpFNFAJiEKR4Jw6tk58cWgVGQg1X3Syqle5+WnwZkWwH0K8UHrsWLvcZUce7N+fPAY/ejgqr6co+Qn8lNl5zxtVhlcNp12yb757LQ4rSO/08YuTk+Hb/Z4kX+v30/nj68LcTK0csxVzzWay7evg6M348vDh5HF7rnkOZe7P0cbhNrIzCbKA8D27HV6rkwR+zkyA99YGXzv2fACPmU40bl6C4MwE+K9fx+13SBE9KpnfAwBJC9bfnhInupOAvLdWgIH50v/tKQF/SyyFQ0XfRiUgCkWCM7Tgabx0gb4ZTpq2Cj/7kPbKmD5rOzZdjN/duPsz6Z3n7MOPitwjR2J8NgArBi9DbvffYJyBn7ZsJtf6Zc7GP3SimpSmPcpGz8ORMS37y3LB7c53hTW5y3FGNu0NkGOeheddO8hzzjGZpPMqUUXyeqsu5LJr+04WAFwaTS/6Wlo85uD2Fo8ZQLd5/49i2oMIWnQMLxXxzQfNzewaO4PmzARhvzr8JzU4gbve0NwIcON68ndkkPJzUcQhKgFRKHoJH+xZSN5knA43vL6dHh5uONcKW+f9OO5uTmfbtolpaWnkWvXYsVJlnu76f8h4vbER4APazLH02DH2OG6IvCuYrUxyotkBSld267W+19QE/9lIt4/1N80kH3vhVc/iYJOJ9UGJKrKEi0sS3t0oQLOzgRsAXEk0Oj6+x+xNMnQbwJ6X6NdrcKpWrD6IcAygF95bK0QLkwzLktmuYHACVwEROj1DBiCfV1EoegqVgCgUvYAJ31mBTocbDv8l2E/hic0vY79MWhHr8fJ/x2u/lRP18wsVq8EAKcycx8+qq2N7Mn64WrPZILihaRDdxgMAh1taIr8L2Z1A5jL/lP4UHpEMUzsdO9lz5lTIosYfdwv2Nf9xtwCPmT+2s7Wps7B9UNe/pcoX7N1+H4LBSasJTZ2FYK1lE1NFL8XB/G76klkKg9N7vYRL6UrkNh0yLHw1W0kpK+IRlYAoFL2E3Pw0SM9OItduu68cObnYAbk26MqgdCyZdeoUcOZoXA/1kE9mstK8EaN0Ja7K98Biyjjv5iXe8/1LcHvS0sHLsOb8CPoxw1G1AZBXA0KgbNBS5KogXPXl3v4L8DvJ3U+AZATJAct2jz1mPslwZnqPmzKXDsL830dfouHDfOHKf9/2sDfRoDA4IdXMzwQoehmy76zsWuTmN7qITADibsmsmEIRj6gERKHoJVw9ZgGePnER2phEo5oJ3EdPXIKXxkQ3mAwVO9OydMPnc6Tuw6FK0oZDo9sNJ7gBVGstZKTW8AeHOiQq0++X7eqHSKkt9M8/VJnhUAnatdVt3gSBQrd5W6W4NVm1qGNSYa0FOLBIkGvmC6STPEzajFaDgfRQUfRCrLWByak/mr17VQ4JnGEngLwi+VDVAnVdKuIOlYAoFL2Im+5cg7XMAGStywV/bSpnDei4tVjz/RNPsUmGDCNiTDwONgxZgYVWK2w5R3isTNqMeWYzbVI3ZS6mWi8FP6DtBPk8po8fEdK5hQhq+8uG4OMG2e4xt+ab/+CCRQCA5A6mnJPLvIPlHzwRrDg0aTMC0GprdcU7sTuzPorEYUhqA2DyGXpR9p3tZgXkjXx6yByA9xECALAY4v/rreh7qAREoehlaABQ6wxuEfhO2vx207WOcBKwPUGWprE32n9zZLEmg+W5vApSpFk6eBm2MsGmDny1Kd9igQe+Kmv/bBacWSJuyaDN/dKNRsjOpJOTPkn6YfLPGYd+ISDrAH1M8ilvgtfvM3r9w0f5JI8Z+LV7PCCc6expWhmXeUXvYVpaGi920ZnYQZTgqsb/WrdKjLRYo/a8CkW4qAREoeiFuJlSvQYAnzRvYgOkeKiCGCWp0NEPmB588A5UR+F0WM7rOtlyc6RoCxZareScyKGiLVicnNz+GUxNTYU7GLWvXUOHssOj0754Muaf053H5/fotbFsOJ+oDbfSt7IhSS4YmXYRBiXR5oVWoxvQQFQMJ21GNOjkWnvg+WHwZ9903S5sE0INpPdySC+cX3/m/cxlVY5uJiDcrBaA93tA8dPshTgvZ0ncbDApFD5UAqJQ9DIGWx5DAK+Ubce1cSlz2BvRt1Ln4Xig1bJixaqvnxWpBvpG+tXRNeLqInrw+sI5PqmKFhuGrEBO3vLg6M2sYd8daWngq0QVWq3ABQdcQAEAcFMYMxtdYeSRx8QQRq3L4fHATZVzybUHvioTnHt6JNhxboVIZ97Pqmu3Y9W128n3sNBqhRyTCXKYY1ONRnYn24IIGqK3Fa4jjN8CwOUEJYo73YqeJ99C/E7eN9p7TXDzRh8+KqStgF2Am/P4pHmTyDWra06RWKgERKHopRiZOYpxKXOQc0B/73dfwp5f0kFmLBhhsdA3d/CqfBXfvIx8UY11PaNAlG+xwJxTi8j36460NLioB8vYFiTNRpvBAMdatwjuMwLwtnJR/OniBlFojU5LxfGx2/B0zfWku/fegnLcX0t7H7x89VrslsNzJ8wcuBhnD1wc8i5ulqZBisEAh4q2kMfKRAtMiMCngJchqiDtKFfqXot0Ro1JQExGt3wWqQtYJc9Lp9gAH0sq3gpFT6ISEIWiFzLY8hhmGI3g9uwibz65+XTbz+0/2YTXFNPu2LHAhAiPDFhE3mU1E/9zVXuqKWrnJOPg6M2YxyRMI6yPYxKTRQy2PIYOjwe+ZIL2X51fI8446VaOdE2DH2SWRa+lQiYjas8H+P3r5DVVdWoywFtvRS3YOXOCbj/hHNv/tW6VeGXEOhwi2Rm2MAmIT+nKUVLBDp3DxJ3eSgeVaNy43rt2gE5O451/lsi9KgBymUpExqFfCFPKWXJNF8KbhHSDouQnyO/9uJQ5yFW3SyRVb4WiJ1EJiELRSzEBgttJDyZ+9ekF4KogF861kIaGPc03Z+jhznf+75Pi0oWe82CYPXAx/uniBvL9Og062QoHAGAzGtkd+DyzGX6avbBnAorb78ORw/4MI488Fnzed0z3KkRRQfcd06N6Xrn59IwP59jue/+OSowWhzCBpP8Ot6ukAoWH2V/2mMNT54pzal0uuP4zNcfCwSkNNjT3B1cbvbmTYzKB63JCq1AoVAKiUPRaDMYZ6GhxQWPd5qBA4oeP70Cjmf76T/jOCnS0cA1A0eOvTeWCm5t463/PFn9/j95ZtDc6Ydr9G3v8xv7nS88Fvc8jrI9jtjBC/dcbg9ZkbRzfSp3Xo69HKmfc7zPeBRog4lWQxWeXCtL4EQAeO7lQwG8+ZJ/vz5eeE88x6mj/WrdK7C0oJ1+n1WCAput2Ba5R7VY3rkfQbXSlg3GsTgT2F27Eg6M3J+z5R5Mt51aI00x1EnQb+92YkZUVxbNSKBQKhSKOuHBuk+BaVwCArYL0BLXO7YKSDwYA+GDPQvY8f/c8sVPfAxxr3UKex5kT64WjhW4TOu/aIU4SFRKuahI3/HG3gD0vxeQc/7NxvVhbs4x8ropvVrLnMP/0YsHN5yiiyIFFoidaz7hWvEiz49wKQXoAAXh9ZxhkClYKRV9EVUAUil5M5sA5+M7/PQ5b5/2YbgNK5/vjvzkTW0neNiEgx0w7CHMzKwAAjXV8i00sSTrlgMqPgwOT3Pyn8JzRE1Qh+dPFDaK/aSY26jp86dgasMZp+l+6sEVQFa1oQSpAAXhNEGOk9NRf06Bs0FLyupiUksIet2HIChyTlMSuc21zAJcrK6GSoPMeMvCjhwU7aH9gkYC/LKcrP1FsPfu0ZbP4tCX4O8C14kUahxDAeQDJeOcSYUKqUPRhVAKiUPRyHlz4ArbY6a+6vdEJr26iA4yWJhec+MfqmAVVFklLEtf/v+eXc8XQgvRonVJIDC14GjUTrZtU3dYGNj9ZXX+/FZvRCCkdZkEKkmYzg/hGSM+mB1GjBWus5zGTalkAAPDOdgHv0NWsUDFJrguZrDSA3B36pfPn2bVtw1aF9x73siRETNiNpE8KAC83CwBw43okZ4hC4EvHVrIiWpT8BMokqqNNvsXCJsQyOMluhUKhUCh6LS+t+rn4zVba3+Evb5axd8bPPiR2OKOEx00rdtkb+XakX5fPiKu7+qctmwUne/lJ8ybxUNUCAQABbugAAIM+mSkGXfbfoMwN45Y/7haytpOehmt7uf6zJwRrrNhZEvHBE4KtDEnwKWv1Kv72lCAVwgDghs/n8O9xFzjv2iFkbVWvnl/TI+/nqq+f5Z/3vbX8Whx/TxSKnkBVQBSKPsCDC19Ag4HetLv57rX40qqfM/MLF+G930Xfz+CDPQsFp3Klu3jpSrc7vu7pRclPIOfRMS5lDvoUmQ7YA1+rFbFdEUum9d9TsLKstz2MMGVu/J0wAMCBReL+qipy6eAlgA+am8N62FSzI6zPKGioPYFgE64b1+MTA2hvmPdHbcK8bpjj9TfNxI9aWqC8lt4Eubf/Apx/enHMfwBklYzhmWfohSgnH/GqWPbz6mfi8rwU8YFKQBSKPsKAXBvsf3sBLRd74iJ5zG33lWMsTP4G5NogZ9iTZIDm8dD3sBP/WC2Kb7oqqucVDtu++YYdjrYZjXDn8fmio3N31bXb8aW8PPi4eZM4PW5H3AWq6UYj/IhLQt5bK6Q7v9GiE6M/tH4DDW20RwsAQF3xTvp97sQsrum6XZjIyUQ4yORjNw3l1b7yrVb4g2TWpjO+mzYfxyQlwX80rCMfo7+mATsQHiU47xgAgDMuF71gcEI0E/V4VSx7IW91XJ6XIj5QCYhC0Ue4+e617M0gJdUELyylkxNHiw5chSRScEnGe797SlR+XEeutTa7wMRICfckZYOW4p3NFlKVZ19BObqEIOcqvpU6D20GA1ADtj3NC3mrkRVmnlyGYK3lD+7GXIS01UmjK2Y+CqxWgEl0YDYtk/ONBhhujb9rKlGZPXAxfmC3Ayej3BW+mzYfqxnZ2wyjEQYy0t3R4lcX+ASVrY54zKoFS6HogPqlVSj6EMItyJaquVt/hfbGBih/LLif+945O6NaBan8eIXg2qz+8ddasDfSbuH/798robBkcVzusKUMSQFuUDbDaIQ5AweSazaDgd9F7WE400QA8A4kf8C0gZgv0E7iXcDl4lWsrJ24SleO2cpeGzfZbGxbWcfqlOIKN3w+R4z9dDb5vv1bPS1YsXTwMuScw7vK/ZmZ8IE9OIB/ZMAizNA0tkICALCsG8lPSLy7UYhLhfx6vLYq9gCUL5KP7lTMFImFSkAUij7EzXevRaeDDtzmbNmFVkY16OL5Ntj4+L9E5cbgcQs2kcgblQG33UebxbUxryMeSNMexQN2O+wg2kNeGbEOJyQnk0HTYMtjGNv93K7z4vA1fABVupKX5Z3ItDp1AdRaQj5mzqlFYtnZpeJf61ax12uWpsFrI9YFnZe/GIAimPdHbcIjRVvIz/NoayssOLOEfO80RHaWoyv0N83ENo8nQD3Ox3fT5qNMFldm+Bky724UR84NC/kwU9qxyJ1DLyDrKj4Z+27afJWo9RFUAqJQ9DHa2tysAaFbvwCbn3ggaG3py7/B5iYXPHv/9IgHZ/aLvGdAciq9c+po2SHm7/h2pE8lony735PsQHqWpkExkex53LtEnsUClDlh3HPjejZwEBN2hxVUcFUXVhoYvDMJSwcvw9zLA9AdW+FqndvFIwMWkecz2WaDmjicwUkENgxZgWtyl5Pv3U+zF+LvL9JzZl3l2/2eRCOTTNzbfwH7mRUnJ8duV52pckzr1y8mT69QJBIqAVEo+hh3PrQZnW109eDx8n/H5kv0bqI1KRUyBtCtQ93hwjl6l/uzD5cLzoBQd4ZuBNYTjKz1wKULwQ7p3+73JA4SRmh2Px+wZjDOwBHWx/Gj5mayehL3/O5dAb97NyLnfcuxeUJmJMgx8shjYuyns0XZGa8iUX/TTLzl2Lz2c1pXy8+rpGmx85foa87Y+wrKAz6HcJhkC72N6Z/Sn0KbwQCfMPLYIcMkGbcMPsse0lU1sK+rVPuRou+gEhCFog9y4VwL21Jltlphw6M/DVqbv/NfMXuwG/7r3+ZE7Cb5xaFVImdoKrnmaNHZ1qzaU01gTY6N83F3GFrwNO4F2qndbH0U/eVcf+Xna2D3eP7/9u48POrqXvz452RfIYFEEhaRKxbcsS5X1KrVay/Pbb3dbH9a6yPasiggYARBDFtkEQg7iN7aYm/18mvReq36w6qoqNSqgIIsgSCERRISQkIWJsvM+f0RgknmnG/IZCaZybxfz9OnOCczc2bmO/M9n+855/ORqx2qfHc22z4AERF/VUj/bVqaXJlgDkBcVz+rXJbMTD/q3l2G9+wp25osFXpv0LfL+ErqvbfTD9v7mBYROVrbcftvfpgyud3F+kJN08+hI92YnKVaK1p5Thz2cTi9tpRz3CjfewDLjxA+CECAMDR6zh9UXIJ5edOk1c+rq25JNbb16JUgPXrZq0u3VWFBhQy5eZbxpBuXYD9pl3zjWw2HznBNYqLjJtnRBQ258u9tsozk/rQnVPbRoxKs+xF2XLpMDdg+1rtvP7ut4TW8uqHd/b6351S1pr85jafTwH3J+U9Zl77ZXJWQINlHZ+i23q+9bEuKQoJDQcbUbQ9pW0HNuC2jtNMSumBmrYcjIvOPzbK2uRz2qADhigAECFMp6XHWvSApafHy6dve6VOH/Xqx6tYj1m8V0stKzLMDIiIDr0gz3p53eplOvcVc/CwY9Y8dpxYVFcn39jyqh+5uPnsUGTFa/bOyIZhqOah+e1Cuqtc6aCtoH7Nl67rrJiXddlnvZ6uR0tLLpfbaIvsuX2EcuX9vz6P6+3lZusxtT1AQZ9hXMq/vbFWvtXOmrwBwytQV9K5fqtIsV/ZPXvWMGmJZPue6+ll1uQ9L69pkk+X3aeNi3Z50uKbEBY0udghenY7HQPHnTDUQCAQgQJj6VZa9mNo1t+coW/pbkYblUe1V5X5G3/6AOW2lx71Gf/WJea1+fx0lg+LHh9TAzSkTz2mt5fKd47XpanjPqCi5Ndm8RK2z2ZZAiUhDMT9LWl7bBvCWft5jivHvRhx8QsvvS801azweeW9QrlpbUmJ8zBdPzNM/6m7eV1TpdnfKQDGUHbtylZrzzUzjZzGzd2+pLDMnU3gyM7Nz9sAEMBXuj1MfNz72qqIc3RkF+f7j/iUh9RuJ8EMAAoSx6spa+d1M7/0eIiKnSmsk54FferVdct10Zds43hZRZ/5ncri+Tr57qzmjTlR06P1svTcoV0UpJRmGegh7LluupmVkGK+G77lsufptmnkmKGi89pr38XPTU8pWBPBcTD0yXU88ZC5geE1CgsiDPYyPXeHxyEU7xunDltmZlMhI6yxHlFKOVb1hNq33TON7dmNylkpKGWdsaxyst7dGh3Vp483T7Z8j9Tg6XXsKU6LrCL0zOQC/+VXWGnV0f4Wx7Sej7MtD/u3/LFJ/+4Ol8FwbxEY+ZA4yLH+/s3qp3l9vT9sbzN4blKsmnGdeOnZrcrKU1682L4eLjAzutLxOVdB9VK+1JFkKOTrNoOy5bLmq9HikyjKT8UlVldxuqTPwbx2RKvV9+7IyayHHEGdLgfvDlMmqvTU6kiIiHPdXdZRJh7OtfbAdx+Esp4953x/CCwEIAMkZfrfxBHrJtTfK/yw2n+BjYyNl3RLzRtNzsffT48bb87fP1ba15fVah+yGTlf1Kj0oLk487jVe71lGzFgVp5QcNQQat3R7TFW63fJusFYIjisU2Ty5zX0rrF2pW9boaLSwX46yDVL6tbIxv6S+Xg5bannkHC223m9RUZGINBQydHr8gLl+qbrKKbtYiHIqLGebPTlXt3efpHo5VFnf4q/Uu61Y2C/H+jqCdQklAABBLW/r59aTuNbapxP8obyndWHBIuN9P/zbVOtjBvVMQCsaU9faZjp2Vi/VH5xq/p6sO5Oa9+OKXL2zemnwvnYfr97b9gE41UBpdVO+0yyDbQPyxsX67Gv4ZIJ2fAwYvXhinl7XJJV0Rzq42/x5zT82S+ed9q7DA6DzMQMCwFGtyyWnKyuNJ/GDu3fKG2v/q80n+G8OnLJuZNdu+8P1cofmT9blO8frHWfqUlyze7c0TWHbmGr30oQJavjBg80G2PccOCAiDevpL91+MGiX6aTH1TrOTNhmFUrq6+WBA94BZ6GhVoeIiHz6sK6otxwDjcFFyzokjbc7BRVJ+XJ2z8r1S5Xcat4A3xZ37psUlJ9VoNzbc6rqrC38538nxVhocErmDOXyeMQWGH1ckRv4z6gdWbeAriw0z+YAOszlN9ykPt/4d2Pb9o83yZH8fW1+zJi4SLngYvMg74KLzTVISouW6EN5ZW1+rmCwo0lRPFsKWRGRA1esbNbW3jXyHaV4yGp1V2qq3GOpk1Bo2RR+f9oT6icpKc1um3Q4WxfXOxQErE+ytxkGe7MGlcsLJXO1xBWKJByy39fP/nbRwtD48PzowthY4zLCQIuIHK36VTYs62vZdmXiRBWtlHE51o3JWeqTygAHCGx6B4wIQACcteFPjxpPxh/97VVZMt576cuPRzysemZUtznn/IU3ZRhv/7giVx/ubd7/kZIWL4mXdMBG4Q7QNNCob7GKrU5rMRZ4O3OF3lb8rbMtOf8pZcryJSIyvlcvsQ30+sbENMuKU6+1XBATa36S+iSRGxbYB3RJ+V6b4mf0maWilJJZ/bpLezJzoXXXJz2q+sSaM1+JNCw/tC1BbK8eveyVzgfHxUmKZTP49UmPqvZm4wLQdgQgAM7yWJY/Tf2vP6pbfmrO4JSSHidJ3S0DRoMj+Qt03nvfGNtcHo/XgLzRX8vLxGlwE2ou2jFOD/7qEd0yLazr6mfV/D59RESkrmWtjeuXnm0LRvVay7iCaV4f4PVJj6popWS/a7lX29WJE1Vik/cgSimZkjnD/Dm3XF7VVEStREe6jQHKvT2nqrGWDGQiIslRoZnYIFi9aFny1D3qYVUZwFor//fkSWNF8ksTJqj+kdHW/VcD4+IcC1+2plNqmgAA0JWYKqCLNGwcLys2b4Te8Y9Z+kj+uZ2ES76xL3lwWpPdWRtcA+XyneP19/OyjK/pf08+rW0zBh+cWhTUG2udPsP7vp5ibTunDFC2fRybJ2vZPFk7bVA37REQERm6e6JuWYW+Ka6O+1fe6WXWY9sfXiiZq22pf01LtNrrrfKF1tfjdFydq+/tMc9KA6GOGRAAzcQlRIkpmDh/kLnSr4jI5UNnqMx/ObflUUfyy61tNyZnGZ+jvH61vjYx8ZweP9iUFpkHvjsuXaZ+17+/cVD049TH1QUxMWJKU3tLt8fUJ1VVgeiqX9yYnGWtjn1NYqJ1c3ZKVJTkFs62BmWycbG2zYDExZaLRFXKBbHmmbgPTi3Sto3ttyYnGwtENprhh5oFyVtHOwZH4WRQ/Hh1fVLg9kXcn/aE6m5ZbpURM1b5Wjfkp/nmVNMuj0dsryfSD3u4Phy8uMvM+gJNEYAAaCajf7K12nhK+gS1d9sc44m40uMxDpib2lm9VF9xY6ax7fiRXGtq3vL9FVK97aRzx4OU09r0C+MeUW7LkrOMmLFq5jfmpWpD4uMlmNPy2uo7jO+VrWybs98blKvilJL3BuW2ecDVLyZG7kvrJgMtAUh+TY3Ylv4M69atQwZ5cZYK7OGqPUueWnN90qPKtNxPpKF4oS/fnb8ONO89croYMC49va1PA4QNfhEBNHNe3yzVo1eCteZGfZ15INc96mFl2+h59r5ayw7XaWNbXXqMZPR/zHiS/yxdy+VDLfsCQtwel6vZJuymPqmqOls/pKkrEyeqW/fuDXznOtjS48edl61YMgpNyciQu1JT5d8sRd/SoqLk5z3MWdc+qqw8+2/T/gF/qPjuGhWnVKtFFMOJ7fPwly+qq43LNm/vPkk9+c03YptpMy2ncgpY5vWdbX0dY3pld8nfLMAfCEAAeDmg66xXjC+4uIe4qs0zHdqtpcr9jPVkXS8Ng2dTW35NjbU/db7VOwwJt3efpDJaVH5Xn4/UIiKfXWLfdD4kPl5GHHwiaN+YO/KydFszdlV6PFLpQ6X7r06fljK32zrgW3viRLN+Ddv72Nl+vVNRcbbNuvndDw5fuUrZKrTD/37eY4rKtCyt++vABcoWrPaLjvZKJWxLI92aYP5+Ap2NAASAl0Hx49XB2lrjFcSE5LHKtkQrKvohVeGQ5cYW1LxcOl/bBp5dbfO5yZCEBGlaBV1f89zZgWpGdLSxhsHbg3LV3anmminB4O1Buaquprv9DyxFFY/ZBukRtcab47aM0s8UF8uioiLrUzVdmvX2oFy14TuLlEjDjEfLZVvPF5uXGCL01GstlWXmmdyUyEhZVZTj1dYndpxKa3FB4PtJ5mBFRCS3cLb1eGltRhgIZwQgAIzK3G6JtmyiLBePMaWqiEjE8RrjSf/g7vna6YRsC1zcInJ3z6ld+srxjclZasOpU7LMMCC6OnGiml9YaLqb3N59kvqFpfhfUDh+m8iGteb+GWpyHDtxYcM/Ns3WsrnJpt9Ns7VEVbb8cxERiY+IELeI7HO5jO3D9j7mleq4UY3HIy0Hm79Jn6ZeKJlr7HNu4WxtGrQiON3efZL6MtJ8IWVMr2xVLw1Zs1q2xUY+1OzYPOxQGNN2bIl41/jpaJ2VwW3o7rbVhUJ4IgABYHRvz6nq7YpTMulwttfJpGf0GPV+k6UrTZ3XN0u97vYeLL6cWmtdflXp8ViDjM4+iXeUjOho+X/l5gxhVyckSKZl/8CE886Tdy1pRzvdT4ap0Rfb96qkbnuoeb8bK5XfPF2lJ3x7DKmEIyI3PWU8PgbGxspPUlLkorg443Nsqa6WlcXFxrbYiAjjGv77054wPldWxnTFuv7QcmNylqqyzK6O75WtnH5fGmcenYKMk7YZ342L9ZL8Hm3oqe/yt5sDZn9kcPPFPy5ewncErSIAAWC1pv9c5bT+eeqR6cYT38HaWq8NvU77OGxLs54vnqPDJQAZ3ytb7a+pMW7CnpI5Q9VrLelfPOzVdmNylhqWn98xnfTBmv5zrYOR0S2zBDWZFbksPv7sRuGmS9Ja+uySpWpYt25yY5I5TXNKZKQUD1ltvP+zlsAEXcvA2NhmSxyb+k36NOuxtc/lkmePP6XfPnXK+tiO+4YsSRP8beAVT6hTpcFbGwgwIQAB4ChKKXngwFSvk1vjidsUhEzJnOGVXta2nMtpSUu91mEzAyLSUDfAVjugtfeiceN6MLJll3LKIPRekz0utoxsjS6IibEGOi2XWDVVr7V1ZsnJORVNRNC4pdtjKjYiwlqIcEvVEm3KdHV3z6lq1HlPqqO2izDvz7cXseyg4KNRtx7jmXVASCEAAdCqKIdBscsha1HjwPP54jk6zvIYtsdufPxR5z0ZNifWPZctV3suW65M1cRPXvWMWtqvn3E9e93Vz6onMzNl4iFzFfvO1lp2qRpL5rS0qCh5t3yh7h87znj/F0/M0y+emKe/OG1O7SzSEJyYXLVzvE6MjLRvenew7dJlypY6GcHp+qRHVbolGE2JjJQSS6HKOd/M1LYZWomolX0nzbU+WmbS8qe2ZpcDACAkjTj4hB5dYE4p+XzxHG3LHHT8SMNA2ukKtm0zu4jIs8efCssT7VsOezriLIOPcQXTgrra9oDtY61Xi52qU5uCsUZvlC1o9fiytQ3dPVE3bpYdsH2s/X371HvZm0jDcW9aEofgZsuq91b5Qt14PDXlNNi/7+sp1jZT5rpwx+Z0NMUMCIBW/dcFc1W8Mv9cuDwe6yxGbEa85J1epu2LYOxXqN8oW6D7Wtq6uh/n51sDjYvj4owD9hX95yivPRVB5MAVK9W+y1cYD5Qfpz5unYW4MTnL2vbDlMlKRCTxhHmJzH7XcmsGrAtiYs5ulo1Sqs3Lqn6TPk3Z9pYguJkC/H/vPkl9UV3ttaS0r6WWiIjIjMpu1rbXy8ra0cPw03ixCuGDAATAOUmLijKu5R/TK1sNioszrq/uHvWwSoqIkCRL+t1PKhfrPZb0qWVut+Pyrq7MdfWzakRamrFt26XLlC3gW9gvR/00f3JYnchrXat1j14JxrZXy8qkZ/QY45vV9D3cd/kKZTtG5brVKpj316Bt7u45VdmC0jK322splq970I45pO7tyqorzPtsRJyzY53X136hAV0TAQiAczKt90wVbzlxXxQbK7ZUl6+WlcmfmlSibuqt8nK5NGGC8cRzsKZGft5jStielC6Lj7cu43B5PHLHmQxRLf0kJcVYTyTU2WoaFBZUyJF8c/pi274jEZGWge+Hgxc7H2uWpVjy/vzAv9eWoo2m5BBoXZnbbZxFXNgvR1W63c2WVsU5pOAdcJk9ze6ItOCdjcwtnK2dCii2x3ZlLhgKAIDPXiiZq18uNQ+45h+bpUcc9N4n4nSycyqUZao/Em6eL56jbfsjnDac9/Mhs1PQeO21c+r74K8eafXvyutXW//GVIDuF/sf13fumxR8750lABFx3iMDu/nHZulzSdrwfUugLyLiNNsYyE3o/mCrEN9eJ+pWBfXrRvBgBgTAObs/7Qk1MDbW2JbnclnreaRERporDluuLtrqi4SbuIgIedWylvx3JSXWQfjhExeKfBScGbFaVZ9krIUiIiKvbDx7+54j3234h0PA0j3qYeushqnY4F8ufFodrHW4gvv6evNzbZqt/TIT4vQYlhmYDysrQ34mZFzBtA7vf2NtHZtPKhdrEZHGVNAmX1myr009Ml33sWRuCwRbmmsnEZGB6V50hTmbmIgImePQFAEIgDaxVTP/w4B5qtLjkWF7H2t2kimpr5ffpE9TP41oXihu16ez9Q8Pmp/jrtRUWdgvJ2yXXzW6t+dU9WpZmTF7TMV31yhbbRW57VElMaWB7l5g/Ow2exDws9uUbG5x1dljSVSwYa2WDWvNA543X7IOhHZUOBx2tZYlNzdPV+LKsN/vXEXUimxc7N23xgKNhpmQKZkzlFOtk1Cwov+cTvmuD46Lsy5XLKqrk48rcvWVCeb9RS+UzLUmyXjHoXBhILSW5tokIXlsQN7zPVvsxT0zLMfpxxW5mpm88EMAAqDN8k6bq+7WaS1NCxAO3T1RNy021zTlblrvRLl8qPnE2X2/va5DSDMNLltx8qpn1OC4OGOby+OxXv2MjnRL6raHQvKk/s5FF0l9nbk2iNT2EHlnpZakfBlXME2PGLLJ+GdvXH9cZNhw4/E1/6p99idvUo3dy89us7dZnqtNbp6uJKrS3OaJaQhQDH6bluaYuhlmY3plqyhpWOrYsu3HqY8rp/0f96c9od4blGv8zH9rSSARDq674ynr98AWsPWNiRFbYgAAAJoxLakSaQg6GlOaXrur+RXbpntBbBusv9g0Q5d80/aBepf2ykYt676w1tAw1S8QEbkjL0u3nJHqEs4EcnfkZWnb8h2n+jJB7aMntW35nPp8pLbVpXiXAMTv1p2Yp23vq8e9xvp+fxnGNUB8WWa1qgsmzUDrCDkB+OT9igrj7U1TLVa0yIz11enTMq5gmp56ZLq27W2IjI6QvV+U+K+jXYHDlXenGhqRSomt+nOw8Gn9/22PKhGRtwflKtvyncw6++nNaWNxp7vpKSW1PYyzZfqa55QlWbDc3n2S+sX+x4P3dQWxD04t0o17Ppqq0VpsOxreq2z4/TNdSCm0VFUPB06Z52weCuKMYQgcAhAAPilzu62Dx8YlIcktptX/MGCeWnfypPyupMS6YT29d6Lc8B9zw37/R0vpg5+TTEt2qx9+4TFukN7wnUVqSEJCUFeUX19WJr5WFHcqHuhx2x/SqbhcUKhPsu43iVbKehz4WrMi3N3S7TEVr5RXMFHpdsu/d59k/C06UtdQ5+Nqw564lrVEwsmtycnG250qw0dEjub3HgBw7pyW97xbvtCYsjduyyjrMhKRzsmIEyp+mj9ZW5c4rC0MyfdtwPaxPvfbNqjZUrVE25bB2NJIB5tf7H9c29K8Dtg+Vp9LGmK0j2lvSCOn4yhQNTYCzZdsWk2ROh1twQwIAJ9tOPgd60lrw6lTsq+mxut219XPqrq6eHOa2HdW6vWWpVkQ+evABco2cyTDM0LuKuK1uyboA1esdOz3lqoluuUG6zvysvTEQ0/qdaWlYqo94/J4zl6hbsmfGaNs+6D84S8XPq0usGzabe09g38sLCqytlVaCq+KiET5sAwpGPiSTasppxm4UAn80XEIQAD47gcj1ZTd5in3KKWM1dFzC2frB3rFy4BuZd538sTIsaIr/dzJrmXJ9iHWehShdlX8svj4Vv/m6sSJqmXI8PagXFWvtRysrTU+RkZ0tPwwZbJxMGWrY+OkrHip8X011RLxpyXn2zMK7blseWiOcoPcHXlZ+o4ze4RSI5vvuFlVlKMbg86USPNunJdL5+vOWuL35gve6bo7ktPx+vMeU6xthbUrQ+p3CwAQBAZsH6tbrsW/c98krT4fqVO3PaRbLrHJLZytnz3+lJ50ONs7Q5NDfQY0YSuIJyLy50/tbb8vDdn3t2Va3saZt53VS3XLqtO1LnsF9GCvUI3O9+KJeVpEjAUeG5f22a7oOy3bAvAtZkAAtMuBK1aq4oX/p9ltZW636GueUyevekZd0OKK8x6XS0ad96Ra2C/Hu+BcwqFAd7dr8MTYK4D/8jr7lXFbjYkQNDSxobDlpQkTVE9381PZAW1efvVu+UKdb1gW2F4MOruWe3tOVR+cWqT/vVs3r7YrEyeqq3aO108dO2a8b3EYb0B3QhV0AIDf9R2+Wfcdvlk3/W9bW68xX579d8qUr3W3aQVaRCQxp0hH51ZwkvIHS82QUPfBqUVnX9e6M1epW3q3fKFu+ndNfVK52Gs/ybl6o2yBtmUTM1WqR9c1i8G02e8qre+LU5DO9yc8MQMCoN2OrL1B1SUmNgsumrbVN6nkXdb/gm//Pf9fzl6t90RESJTLFeCehoeLLn1eLtoxrsud1G/p9pgSadj8XWPZ8JoYGWndDHtBTIw1rWprfpgyWY0670njfZvWvkHXsMyhON6QhARre1DXmAm03yZZvwe/SZ9mbbsyofW9YAAAGCVnH9aJOUW6Z1aeNgUi6eN2eO0VEWk4Yd/39RRNCkf/ckp1HAzucBiovXhinrWC+yeVi/UHpxZZ0+zaZj9ERNwee/VqoKmhuydq0x4QkW/3iJj878mnOcYMbL9H5fX2/Vro2pgBAeAXFTn9VNX3V0jpPblStOpKc/GuNb/1uu29QbnKpXVYF+9qjzv3TTKewOuufjaor8q/U1FhbVtbUiLxEd6np3Un5ukvq6vlWF2dXGkoAHff11P02hMnrI9rW7ffFk4Ze54vnmNd/oXQ8o/iVPnDgHnG79B/Oxxjg5vM9oZbMOKU4OHzwYONt3ePejiof6cAACFgwPaxOs5ypavpfg/4z7iCadpavPGVjdoxK1YnWmVZwtJYxM2pcrLTVVNbQHbtrgnaX8tjnIonvlG2ICjfb7SN7TgSaSimamsL50Kq+13Lja99v2u53lltTmWN8MUMCAC/OVZXJy5LkcGyYX+WU//+gvF+kStqtFpVxwnKByv6z1Er+s+xXkWMPTioI7tzzsb0yjb2uc+ZGgpXG2Y4GpXU1xuXwaw7MU8P79lTph6Z7tVWWFcnX50+3Y4ef8vlUHDNVn8EocXVsoZRk980185pxvukbntI2wpghoMPc3cYb49Tylqc8ZPKxfzuhykCEAB+46qLb0gRa0r3Wn2+SNkQkVc3eJ1w3PF1omNrRNYWcjLyxXO1Wp6r9X7vfnabUh5L5fQgdXfPqY4D+IKaFbqfRMm9hr9zi0hSZKTM6zvbq+3wlatU8ZDVfgkOjl25SlFPpGtrupRKROSezDr5xf7HtYhI8mVzjfc5WfSv8teBC8IyAB26e6J+4S7vtMUiIn1ix6lB8eON78sb5eUB7RcAIBxsbLialbrtIS2bZjcfoL1zZu385sm6adrF9C8e1vKn/Ib/bvx/tJ1DCsxQ1nK5VYHDwH/EwSccN7b7s18iDSl//f2YCA7WLHKbJxuTaYiIxC4oD9/jYf1HWtZ/ZHz9jdXjTe77ekr4vmdhjhkQAP5TnyQiIifL+4vU9vj29s2TtXTbJSIiQ1Pr5PPq6rNNxacyJPrihQ3/EVXZsG8BbeeJEeMsSBBqS4aulptU/1FpL6b4RZPjallRjm5ar+GyFle0/eF2h5S+ta7V2pZ1y1ZPBMHh8p3jtS0pxrA0j6RFRRnbuv1gaiC7FdRGX/umyF03Gb8PlW77LOxfTp4MWJ8Q3AhAAPjH+/O/HVTdOkVJbQ+R19c33FZyU8P/pKFmQt2xO5oFGmczNt09REnv1zqy113HyBglI2O6/PKPjx0CkIO1tWf/Pb5XthqSkCAiIi+XztenHfZttIulIn1M3MMqMmK08fMYdd6TymmTMzpXSmSknLzqGeNnN6dPH3l7UK6xzVZ/JhS1NWFItNh/eh5ISLW2uYqHtuVp0IUQgADwj+rzRX4w8tuz0H/8SknEmQGhJ6bhv5v+rSuj4d+N/39Gv/i6LllEryMk5hTpxJyioH/v2pMieE1JiddtyVtH6+gto3RFiyutP059XF20Y5yeeeyYbKmq8vUpnf3nf/r0Wl4vL++yFetD3ZOZmda2I02C3Jbe+c53HB/XKXtWsDk1p3+bjus6sb+04hiHl93j07Y8DboQAhAA/tF0yVWjU5eI/PGQlsqBzW//5XVK6pMaNp0XDmvWdPjKVSrJUAMCravK7qWqsnt13VmQzZN1XVUfr5srKntL3el0MS302Fc0SHbsv92adauz6GueU1GFzgNWdI4MyxKrpkz1Xva5XI73uSguTu45s5HdnzJGb+30wOb9CvvM5JBdu6xt3+vm/6WRCA2c5QG036sbtHhivG+/e4gST4w5OKlParj9zL6Rprbl/diYLQvhbViaR+7oZciac/N0dW2PSvPMyr+NVZL2UcD7NrrAsgF+baG2ZXerT4gSeWkXx3mQMRW5bNS4zO+r06e99vJUtUzd28KOS5epvjGG38l2Klzz3U4Prvfs/Ym17aneva1tP0lJ8X9nEBIIQAC0X1yhSEypvb2b4QrYgz1UVHW9RJ+M9m772W1KCn/gv/6hS7ggNsa6/n5Cr172O5oCYD9b03+ueRA4PENFn0g0Z0gaGaOk7/pAdw1tZMtyJSLyzqlTItJQx+aTqioZtvexs3/7m/RprQYCC/vldHqwEBDnbbQ2XRYfb7y9xv2MvubMPi2EHwIQAO02YvBekR/dZT6xRlWK1xKsM6KrqiSmSeaiZmLKQiarE/zk04cdP+97UnvIlqol2lS8rKS+Xr6351Hj/eOSD3TIvqK4+aU6bn6p1/PUZSWrmsEfmO908/SuOSANYaPT061tTRMdfHX6tOx0KG6ZWzg7fH6/bnrKehzf0u0xY1ts5EPK1oaujwAEQLu9bism9c5KLee/JOlDZjfUBmkiOfuwjnK5JLqqSnpm5XmfqB/soVQd56awcp1zocANp07J1YkT1fVJjzb7u48rcnXf6Gj5dQ/vmY4tVUu06+pnVVxEhDxwYGrnDQhjSptV026psHZl+AxWg9yo8540HocDto/VTVPKfnbJUpURHS135GXpAdvHen1+WRn24NKpZg0AADgHtivP8tIufTbdbosig41BR9qEXTpzxGfWk3GvMV9yog4DSTO/8flzfr54jn7LUhTwjbIFZ2+fdDg78MfSc7XWPR/37H+8WW2SpsLqankXM2zvYzoQm8sBAIAv/v5cs5Ny1NLqs//dck28UxACJOYUaVulaafA4uOK3I4/rt58yfic2Udn6H5fjjG2TT0yXWdbghO/2USQEyjMaAAA0IGMy6fOSM4+zEkZQeXaXRN0piUI8JfUbQ/pwV890ubnSN46WrdcqogQ8ffntGz03pskIuLLsQAAABykTbCkEX1npZb1HxnbMkd85rjsCgioNZ5OPfZOlS6zPr81na+ffHBqkTbVsBARsX1f0X4pU77mvQUAINCcAgyCD4SDobsnGo/zI/kLtNuzxtjmFJy0RWnREuvjlNevtj+Hw0Z5+K7btALdnn1OQFdDFiwAHYrgA/7UbVqB7jatwHhMOQ34IlfU6MgVNQE9Fn+bliaXG2pK9B04WUVGjFY17me82rr1GK+2fzyz3f1KSYuXozUrjI+z8vhxsW2Gj44vlugto/iO+tmpOf1V5cze9ixvvy/V8udPed8BAGhNxuitbV5i1a7lV63UiUB4ctprlJhTZG5b94WW9+cH/HhK3jra+hxOmZPWnZjX7r7tdy3X+13LjY9zom6VtgUoyVtHWzfLA4A/MAMCwGeFa77rc6EOaxDyykbrRtx+sdQF6RRBHvhV5PSzHhieiAiJn1Ps3f+7hyi5dUrAD6iK765Rzx5/yvj+XRAbK1uqzEulTtTXi+1+5+rCuEfUX06eNLYV1tXJJ1VVxrY+MTFS354nhiNS9gIAEADnsv/DcRbEksZURERee42TN9rEaYYk0MuwRETGFUzTEw+Z91a4qlfpyjLzTMTzxXPa3bfsozP0/GOzjI/z0/zJ+s59k8zP8enD2mn2BgDagxkQAB3q2H9dq0RE6hIT7Rm0Sm6yP0BErXOAgsAIgff8XUsxQhFpWGNvEFFbG/B00XEREZIUGWltdyeZ22485BGP27xZ/Vzl9Jmlrk1IENNSrL8OXKBGp6eb73jdajW8Z8/2PHXYY6YDAIBQYxkwikhIDIZDkVNNF/njId2ymn2webl0vp7zjWUD94a1WjasNe956IB6NaMLntAPHJhqrdb+QslcY9t+13Kdd7r9mbG+tCz1mnQ4W48rmGZs87jX6ALLPhEAAIAO1efBf/o0KGnL5nP1+Ugtn0ww/32QD4RD2dQj0/WqohzrspwO7k6bWQMQcd4Q3hG1QX6aP9n6HLasVHmnl+nC2pXt7tvO6qV+CWQAwB9YggXAr9ocnPwpX8sfD3ndR1/znJIjd5nv8+uBqteYLxlMBcC8vrOVdbnQdauV7Up9sJjWe6aSvz9n7ONTffpYs0vdefvjjgGCPyRFRIhtFiRKKZl6ZLpX26D48Wp/TY1MOpzdrr5dmjBBRSklO6uXBvXnByA8EIAA6BB9HvynjnB7vBt+PVBFXzrHeJ/0gS9J7IJy69p9aooExv1pT4R2urGkfOPN43tlq2RLcPW3ixaqYd26BbJX8t//Ml+JmPcGTOs9U22rrpbv52V5td2YnKVcHsN3p42K6+ulXvOVAQAAXUifB/+pbTMgvi7bcrqfrQ4JAstUQC/Y2OpY5J1eZq1/8eKJeda9GH5lWVp4R16WHrb3Mevz2/ZxAECoYQYEQJv0Hb7ZOghSHrcc/f2/Gq+eK4/b5+ckCAkuJfX14o99CYGUaJnpGBQ/XiVXmWcT7u05Vd3bPTWQ3RIRkeQYl1y7yzsIeXtQrip3u+UqQ/V0EZGkyEj5uCI3qN93+F8mRSEBAOHOKQDxta09WIYFX9gKAHaU7+151Pr8tgBExHmTPQCECmZAAPhFZwQfIt/WFQFaevOFidbjbkClyKG8p43t84/N0qYN4f704eDFyqmopm0J2criYudsXgAAAOGi7/DN2hZotCUAcXoMW5uv+0vQ9b1bvlC/ZSlQaNsLIiLy7PGnAn9Mvb5eW2vabFys5f355rbNk7Vsms0xDyBkMQMCwC+OrL1BHVl7Q7tnI3x9jEDOsiB01UtD+luTwvp6eaNsgfG4uT4xUerrArzZ/kd3KfmPX5mP99setX8PKgeKuDIC1SsAAIDQ56/goLOWeSG0Ha1ZYS3ml1ton0l4udQyA+FnjkuqbMU433xJy+vrOea7qOgto/hsAQDwFYEBgsG7lmVYTiYeelJPPPRkwI/fOKfB5kcOz785sIUT0XnU5yO1raAmAAAAQkD20Rk6++iMNg/oAl0dvZG1b58+rG0b0uWTCVpe3cAgtYtyDEz53AEAAILfsqIcbdtcfqJuVacO6F4una9fPDHP2Ic7903Sl9tS8/4pX8vvSxmMdkWvr9dOmdKAUMYmdABAWIhTSoZ1725sS4qIELdnjXWw90nl4oAOBH/eY4paf/Kkse1vFy1Uto30Un2+RFYl+qUPg796hMFuMPnRXUpKr7M2R66o4fMCAAAIdjXuZ3Sta3WbB262bFn+9ov9jxuf5579j2tT9XQREdmwVsvG9gdIQ3dP1HfkZTGoBQAAAPypusKcEUtEJH/7XGPbiyfm6XWWJVL+NK5gml5WlGN8HsdsWc/VEjiEoUzb/iAgyLEECwDgbd0XWv54qEsObqoraqWwYJHxtUVFR8iOf8zyaru351R1bWKi5J1eFtD3ZEX/OSolMtLYVpH/oMhLu8zP74kSWePpkp8XGpgyCh67clW7ay+Fujv3TeK4D0EEIAAAb3cPUUlfR1mbE3OKQvakn9b7UbXr0yJ5/xXvDFcXXDxFna6sM96vXmvJiI4OeP/u35omxhSsv7xOiVgGXKMj1Jz/nC0dMUuD4BPO6c7/dtHCsA/CQhEBCADAKLaiQjJHfGYc2Ci3W1KmfB3Sg57NbxYYb6+v88imV6d4vbZB8ePV9v93WP735NOBfd0/GKmO3lIjBTUrvJ/nV5eoOMuG9Gm9Z6rUKHvQiNB2ZO0N1oG2UxsAAEBI6fPgP62D7dRJ+0I6AFk64T792u/M6W3/vHyM/u8F5joMvhQ1bKujNSt0eX3bN8sP3T1RW5dpAQAAAKHAFoQkZx8O+cHu3/5gySwlIqseN7d9/m62/uZA4IOQ7R/P1GXFS43Pc0delj1j1fqPtLyyMaQ/F4ikj9vR5s8wnJdiIbSwBAsA0CpTEFKR009JUr71PmkTgj84OV1RJ39ebs4kVHO6WBY8dI9X2zW356jqijpZMt4hK5UfXHHjTKVSY4xtbw/KtS+5iaoUiSkNVLcQxMJ9KdY9ljTWAAAgBNn2gsgaj7USd+SKGh0K6WFfyrUHEovG/MrYljtupM4ZfnfAX9u6E/O0LzVIhu6eqBmMAQAAoEuKWlqtu00rsAYhviwl6Wi540Ya+7j4kXu1aRZERGT2/b/Qs+77ZcBfW3XFSm2r0h63ZZRO3faQsW3S4exOfd9ZDhRYTvuzgGDHEiwAQLukHDwoicePG9t67N0rER5PB/eo7e58sL/s3TbHa0D36PIXVXVFncwfNdyrbfoLf1EJyckB71tC8lj12wJzxq74iAgZ3rOnse1Iba21sjpCyIa1xs/w6O//VYVCcA+YEIAAANqlZOklKsLtMV6RLV5xuRIR6TXmy6AeKH3nqmkqKtp8SkxO7SHxiYnGtqpT5TLz13cF/LUdrK2V+772Tg188qpn1JOZmXKibpVX2/9c+LS6KSlJJh56st396zt8s27rjEa470foCI3fLwAAgLBkWxKSMXqrTs4+HNQBSCjY71quD+WZa5AcNdUMkYZlWLOOzuiUAAQAAADoNPFzikN+8Lrtg85PbXuqdJmxD27PGm0LQn6aP1kP/uqRdvd98FePaJZ0AQAAIKg4XSUP9mVY7bH945m6xv1MwF9fYcEiXV2x0hpoWO/4x0OBf++dMp69+VKX/ewBAADQyWxBSPq4HToUaoO8Vb7Qp2VLpn0YgZC81Z422Jb5KnPEZ4FfQvX7Ui1/yrdm6wroc8NRxuitvP8AACA8pU3YpZNmfhP0gyFbAPJW+UL9Vrm5Cnph7Upd1QGzIHFbRuloy4D+f08+rft9aS6smD5uh+6Zldfu/kVvGaWtAcWfP9Wy/qOg/3y7KvbpAAAAtNBtWkFIBCBH8u3F//a7lmvbbEd9XeADEBEReWWjllcse1Jee83aB1tw0lbWPSXrP9Ly+vqg/3zDEcEJAAAIW6GSUeldy0yHiHP19A7x6gYtr24w9+GPh6yzEPfsf1x/Py+r3X1fVpRjfYzUbQ9p2TQ76D9fhJ+opdUclwAAhKNQCUDWnZinbRu7C2tX6rzT5oxUHeXjilz9cul8cx9eX69tBezu+3qKfuDA1MD2/X1Lv9DpQuG7F0ip2x4K69cPAECXYBvQOA10+jz4T5054rOgHwhctGOctY/l9autbYW15kxV/uY0S+MUBGT7oTYIgpvT9zLcgxAAANCFOQ12bIULQ4ltKdL8Y7P0sL2PBfz17Xct1y+emGd+nk8f1urzkdY+OGXTQmggmAAAAGHLl0FQVx88TTqcrccVTAv46xu29zH97PGnrIGQ7X6ZftqQjs7Vlb9D6LoiOrsDAICuoa0DoSNrb1C+3K8z5Ba2fVN1hcctX7lcgehOMxu+s0hdn5hobBves6dMPTLd2PfukZEsxeoCGr9HAAAAYccpkAj59ejP1WrHSt8OBmwfG/DXd/xIrq4sW2F8ntEFT2jbTMi75Qu1LUBBeAiJ7x+6HGZAAAB+02VnQUbGKBkZ49OV5gNXrAz4Ferz+mapghi3fHBqkdf7uKb/XDXxvPOM93unokIyoqMD3T0AAAAguAR9ABIC3i1fqE0BiIhIWfFSx/fX7VnD+x+mQmYWEl0KMyAAgKAQKoOgYO3n7d0nqa3Z+cYq7inpE9SLC+fq2ff/wtj3t/47L/AdRFAKmVlIAAAAfwuVAVCw9/ObA/baIKunDLe2vVAyN6hfF9rPl31aQCAwAwIACApH1t6gQmEQFApZhz5/N9v4Pp4odMmaaQ8Y247V1XVMVqxPHw76z7irau07FgrfP3QNBCAAAL/zdZATCoP7YNd7wCQVmxAle7Z4F0nMXrtOXXJtL9n06hSvtimZM1SUn/rgmPnLleFYpR2BZ/oOshQLAAB0aVyFDTxX9Srj+7jp1SnaFIA0WjH5/sC+/+/P17Kp7XVV4D98/9DZmAEBAHQKBkGBFRMbKfV1z3i9jzf/ZL6KiomQLzaZl1u5az2B7ditU5TcPJ2Zrk5m+46FylJIAACANiMACbwa9zPa4zan2DUt0WpUWGBO5+tXf8rnMwbCFDMgAIBO4bTfg70g/hGllPyjusrYVlfrkdKiJcYgICU93jh74lcZG4SlWAAAAIAfBcNMzvPFc6x9qHE/o6/aOd7YXuta3el9BwAAABCCRhc8YQwmrto5Xqd/YU+LO+Kg+X4AAAAA4ChufqkxmBi6e6KWDWuNbclbR+vkraM7LQiJXVBOAAQAAACEIlsAIiIiL+2ytmV+OYYgAIBfsQkdAIAwEH/ihKRNsAQav7pEyd+fM1dIL76Y6uUAAAAA2q7XmC916qR9xmAiY/RWnTF6q7FNfT5Sy58/JQgBAAAA0Da2IENEpM+D/zS3rf+I4AOA37AECwCAMBLlclnTA9fHx0nmiM+82+66SfUdvln7K62wr49jDZAAAAAABC+nYKK1tsD2rOE5CDSAro0ZEAAAcM4CHYQcWXuDOvr7f1WBfA4AAAAAHaw9syDBUOEdAAAAQIjxdbmVP/eCEMwA4YclWAAAwCjQQciRtTew1AoIQwQgAACEqcYAwBRM+NrmC5Z7AeGFAAQAgDDmNAvRETMU5xLoAAAAAOhifN0L0hG1QZgFAQAAACAiHRccEIQAAAAA8KvOzsoFAAAAIMzYgol+933ouNyLIAQAAABAm/m6r4SMWQAAAAB84msgQQACAAAAAAAAAAAAAAAAAAAAAAAAAAAAAEAHyPxyDBv8AQAAAHSM5OzDOn3cDoIQAAAAAB0jc8RnPgUg0VtGEbgAAAAAaLvEnCJrMJEy5WsCDZyTiM7uAAAAAEJDVT97jOGJjHS8r1PwAgAAAABmL+3S8rvKNgcTcfNLCUAAAAAA+MCHAAQAAAAAAAAAAAAAAAAA0AwpdgEAAAB0rFc2EoSgTUjDCwAAAN/1XS/yp3yCEAAAAAAdZONiAhAAAAAAAAAAAIDmWMIFAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABwxqsbdGd3AQAAAAAAAAAAoIM9V8sMCQAAAIAO9OZLBCEAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAEPT6Dt+sO7sPAAAAAMJIv/s+tAYhTm0ELwAAAAA6DAEIAAAAAL9jFgQAAABA8Ng0myAEAAAAgP/0u+9D7TQTAgAAAAAAAAAAAAAAAABoNzalAwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAoPNRvBAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABAl9Hvvg/1/weGXGhPq5k9yQAAAABJRU5ErkJggg==\n",
      "text/html": [
       "<img style=\"margin: auto; border:1px solid\" src=''/>"
      ],
      "text/plain": [
       "<xarray.Image (y: 800, x: 800)>\n",
       "array([[         0,          0,          0, ...,          0,          0,\n",
       "        4290993437],\n",
       "       [         0,          0,          0, ...,          0,          0,\n",
       "                 0],\n",
       "       [         0,          0,          0, ...,          0,          0,\n",
       "                 0],\n",
       "       ...,\n",
       "       [         0,          0,          0, ...,          0,          0,\n",
       "                 0],\n",
       "       [         0,          0,          0, ...,          0,          0,\n",
       "                 0],\n",
       "       [         0,          0,          0, ...,          0,          0,\n",
       "                 0]], dtype=uint32)\n",
       "Coordinates:\n",
       "  * x        (x) float64 4.826e+05 4.83e+05 4.835e+05 ... 8.617e+05 8.622e+05\n",
       "  * y        (y) float64 6.273e+06 6.275e+06 6.277e+06 ... 7.737e+06 7.738e+06"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib widget\n",
    "import datashader as ds\n",
    "import datashader.transfer_functions as tf\n",
    "from matplotlib.cm import terrain as cmap\n",
    "\n",
    "df = dd.read_csv(str(outdir)+'/*.csv')\n",
    "\n",
    "pts = ds.Canvas(plot_width=800, plot_height=800)\n",
    "#agg = pts.points(df, 'lon', 'lat', ds.mean('h_elv'))\n",
    "agg = pts.points(df, 'x', 'y', ds.mean('h_li'))\n",
    "img = tf.shade(agg, cmap=cmap, how='linear')\n",
    "img"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make grid data from AarcticDEM. It is needed to extract value by Icesat2 data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "driver GTiff\n",
      "dtype float32\n",
      "nodata -9999.0\n",
      "width 546\n",
      "height 546\n",
      "count 1\n",
      "crs EPSG:32607\n",
      "transform | 95.79, 0.00, 650017.17|\n",
      "| 0.00,-95.79, 6579617.67|\n",
      "| 0.00, 0.00, 1.00|\n",
      "Data minimum, maximum( =  nan nan\n"
     ]
    }
   ],
   "source": [
    "# print out metadata information\n",
    "import rasterio as rio\n",
    "from affine import Affine\n",
    "from pyproj import Proj, transform as transform\n",
    "file_in ='/home/jovyan/shared/data-Jun/dem100m.tif'\n",
    "src = rio.open(file_in)\n",
    "for k in src.meta:\n",
    "  print(k,src.meta[k])\n",
    "\n",
    "# Retrieve the affine transformation\n",
    "if isinstance(src.transform, Affine):\n",
    "     transform = src.transform\n",
    "else:\n",
    "     transform = src.affine\n",
    "\n",
    "N = src.width\n",
    "M = src.height\n",
    "dx = transform.a\n",
    "dy = transform.e\n",
    "minx = transform.c\n",
    "maxy = transform.f\n",
    "\n",
    "# Read the image data, flip upside down if necessary\n",
    "data_in = src.read(1)\n",
    "if dy < 0:\n",
    "  dy = -dy\n",
    "  data_in = np.flip(data_in, 0)\n",
    "\n",
    "print('Data minimum, maximum( = ', np.amin(data_in), np.amax(data_in))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate X and Y grid locations\n",
    "xdata = minx + dx/2 + dx*np.arange(N)\n",
    "ydata = maxy - dy/2 - dy*np.arange(M-1,-1,-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Multiple csv files merge into a csv file "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "import glob\n",
    "appended_data = []\n",
    "for infile in glob.glob('/home/jovyan/shared/data-Jun/Data1/*.csv', recursive=True):\n",
    "    data = pd.read_csv(infile)\n",
    "    # store DataFrame in list\n",
    "    appended_data.append(data)\n",
    "# see pd.concat documentation for more info\n",
    "appended_data = pd.concat(appended_data)\n",
    "# write DataFrame to an excel sheet \n",
    "appended_data.to_csv('/home/jovyan/shared/data-Jun/Data1/appended.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "glas_fn = '/home/jovyan/shared/data-Jun/Data1/appended.csv'\n",
    "glas_df = pd.read_csv(glas_fn)\n",
    "\n",
    "glas_gdf = gpd.GeoDataFrame(glas_df, geometry=gpd.points_from_xy(glas_df['x'],\\\n",
    "                                                   glas_df['y']), crs='EPSG:32607')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check where icesat2 trak go onto ArcticDEM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ec89ceb2e43c4d7aa57f34ada5ea5f7a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "NameError",
     "evalue": "name 'glas_df' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-12-0bf7c8877426>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrtm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mextent\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msrtm_extent\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0mx1\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mglas_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'x'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     13\u001b[0m \u001b[0my1\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mglas_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'y'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmarker\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m's'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfacecolors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'none'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0medgecolors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'k'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'glas_df' is not defined"
     ]
    }
   ],
   "source": [
    "import rasterio as rio\n",
    "from rasterio import plot\n",
    "from affine import Affine\n",
    "import matplotlib.pyplot as plt\n",
    "file_in3 ='/home/jovyan/shared/data-Jun/LC08_L1TP_060019_20180703_20180717_01_T1_B8.TIF'\n",
    "src = rio.open(file_in3)\n",
    "srtm_extent = rio.plot.plotting_extent(src)\n",
    "srtm = src.read(1, masked=True)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.imshow(srtm, extent=srtm_extent)\n",
    "x1=glas_df['x']\n",
    "y1=glas_df['y']\n",
    "ax.scatter(x1,y1,marker='s',s=1,facecolors='none',edgecolors='k')\n",
    "cs=ax.imshow(srtm, extent=srtm_extent,cmap=plt.get_cmap('Spectral'), interpolation='nearest',\n",
    "               vmin=-200, vmax=4700)\n",
    "cs=ax.imshow(srtm, extent=srtm_extent)\n",
    "cs=fig.colorbar(cs,ax=ax,shrink=0.9)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compare Icesat2 data with ArcticDEM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  Z      Z1          Z_Z1  Unnamed: 0  Unnamed: 0.1  \\\n",
      "0        463.316101 -9999.0  10462.316101           0             0   \n",
      "1        461.939575 -9999.0  10460.939575           1             1   \n",
      "2        308.686035 -9999.0  10307.686035           2             2   \n",
      "3        230.033829 -9999.0  10229.033829           3             3   \n",
      "4        228.711166 -9999.0  10227.711166           4             4   \n",
      "...             ...     ...           ...         ...           ...   \n",
      "1912106  674.867371 -9999.0  10673.867371        8239          8239   \n",
      "1912107  675.978394 -9999.0  10674.978394        8240          8240   \n",
      "1912108  568.879456 -9999.0  10567.879456        8241          8241   \n",
      "1912109  567.561646 -9999.0  10566.561646        8242          8242   \n",
      "1912110  559.518738 -9999.0  10558.518738        8243          8243   \n",
      "\n",
      "                     x             y        h_li       t_year  \\\n",
      "0        803548.121030  6.420887e+06  463.316101  2019.970293   \n",
      "1        803541.130006  6.420927e+06  461.939575  2019.970293   \n",
      "2        800409.303992  6.438650e+06  308.686035  2019.970293   \n",
      "3        800356.263802  6.438945e+06  230.033829  2019.970293   \n",
      "4        800352.742828  6.438965e+06  228.711166  2019.970293   \n",
      "...                ...           ...         ...          ...   \n",
      "1912106  555495.117847  7.677241e+06  674.867371  2019.901949   \n",
      "1912107  555492.448820  7.677261e+06  675.978394  2019.901949   \n",
      "1912108  554922.348875  7.681383e+06  568.879456  2019.901949   \n",
      "1912109  554919.554331  7.681403e+06  567.561646  2019.901949   \n",
      "1912110  554911.090823  7.681462e+06  559.518738  2019.901949   \n",
      "\n",
      "                               geometry  \n",
      "0        POINT (803548.121 6420887.243)  \n",
      "1        POINT (803541.130 6420926.713)  \n",
      "2        POINT (800409.304 6438649.509)  \n",
      "3        POINT (800356.264 6438945.412)  \n",
      "4        POINT (800352.743 6438965.142)  \n",
      "...                                 ...  \n",
      "1912106  POINT (555495.118 7677240.871)  \n",
      "1912107  POINT (555492.449 7677260.699)  \n",
      "1912108  POINT (554922.349 7681382.797)  \n",
      "1912109  POINT (554919.554 7681402.607)  \n",
      "1912110  POINT (554911.091 7681462.026)  \n",
      "\n",
      "[1912111 rows x 10 columns]\n"
     ]
    }
   ],
   "source": [
    "ex_ele=[]\n",
    "for i in range(len(x1)):\n",
    "    idy = (np.abs(ydata-y1[i])).argmin()\n",
    "    idx=(np.abs(xdata-x1[i])).argmin()\n",
    "    ele=data_in[idy,idx]\n",
    "    ex_ele.append(ele)\n",
    "z=glas_df['h_li']   \n",
    "dif=z-ex_ele\n",
    "df2 = pd.DataFrame({'Z': z[:],'Z1' : ex_ele[:],'Z_Z1':dif[:]})\n",
    "df_concat = pd.concat([df2,glas_df], axis=1, sort=False)\n",
    "print(df_concat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, Plot on the map "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3178099a35f54dccb086b15ca3696059",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f38ed808910>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#import matplotlib.pyplot as plt\n",
    "#fig, ax = plt.subplots()\n",
    "#fig = plt.figure()\n",
    "ax = df_concat.plot(x='x', y='y', kind='scatter', c='Z_Z1', s=1, cmap='RdBu', vmin=-50, vmax=50,zorder=5)\n",
    "#ax.set_title('Elevation change',size=20)\n",
    "#ax.set_label('Elevation change (m)',size=15)\n",
    "\n",
    "\n",
    "#file_in2 ='/home/jovyan/crossovers/Jun/LC08_L1TP_060019_20180703_20180717_01_T1_B8.TIF'\n",
    "file_in2 ='/home/jovyan/shared/data-Jun/LC08_L1TP_060019_20180703_20180717_01_T1_B8.TIF'\n",
    "src1 = rio.open(file_in2)\n",
    "srtm_extent1 = rio.plot.plotting_extent(src1)\n",
    "srtm1 = src.read(1, masked=True)\n",
    "ax.imshow(srtm1, extent=srtm_extent1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bc5c1b49b1a2496fb6909691c03a6f3c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.957275390625\n"
     ]
    }
   ],
   "source": [
    "f, ax = plt.subplots()\n",
    "df_concat['Z_Z1'][df_concat['Z_Z1']>=50]=np.nan\n",
    "df_concat['Z_Z1'][df_concat['Z_Z1']<=-50]=np.nan\n",
    "df_concat.hist('Z_Z1', ax=ax, bins=100, range=(-50,50));\n",
    "ax.axvline(0, color='k')\n",
    "ax.axvline(df_concat['Z_Z1'].median(), color='r');\n",
    "print(df_concat['Z_Z1'].median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e3f318a23e744f2393ced8c30638a7b2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df_concat.plot('Z', 'Z_Z1', kind='scatter', s=1)\n",
    "#Add a horizontal line at 0\n",
    "ax.axhline(0, color='k', lw=0.5);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It would be helpful if we clip data with Polygon data in order to distingish between Ice and Ice free area."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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